dl-learner-discussion — DL-Learner related discussions

Re: [DL-Learner discussion] tips on large knowledge bases

[Jens L. <le...@in...>]

Hello,

Am 19.02.2015 um 13:26 schrieb Diogo FC Patrao:
>
> Below is the configuration file. I'm sorry, but I can't share the sparql
> endpoint, because it's hosted on our intranet. Besides this, I changed
> the script /cli/ to increase Xmmx to 25000M.

Is the data itself confidential? Otherwise, you could also share the 
dump behind it via dropbox etc. (not necessarily public, just sharing 
with Lorenz or me would be sufficient as it could save as some time to 
look into the problem - we can sign NDAs as well if needed). We can then 
load it into an endpoint here for testing.

Also in the conf file, it may be good to specify some termination 
criterion (e.g. 5 minutes via alg.maxExecutionTimeInSeconds = 300) to 
avoid the algorithm running forever. (If it doesn't find a perfect 
solution, it will indeed always run out of memory at some point otherwise.)

Recursion depth 4 could be quite high depending on the data. Trying 
lower depths first would be something to test. (It depends on how deeply 
nested you expect the learned constructs to be.)

Generally, we are currently looking into various approaches and 
algorithms related to scalability (also across several machines), so if 
you like to involve us in the cancer patient use case, we'd be more than 
happy to do so and could run classifications on larger machines here. 
For us, it would be a good additional test case to verify whether the 
improvements we are planning at the moment lead to good results.

Kind regards,

Jens

-- 
Dr. Jens Lehmann
AKSW Group, Department of Computer Science, University of Leipzig
Homepage: http://www.jens-lehmann.org
GPG Key: http://jens-lehmann.org/jens_lehmann.asc

Re: [DL-Learner discussion] tips on large knowledge bases

[Diogo FC P. <djo...@gm...>]

Hello Lorenz

Thanks for your fast response!

2) I read on the manual that dl reasoner has a built-in reasoner; It could
be pellet, hermit, fact++ or the owl api basic reasoner. I was wondering if
it can be turned off to save memory, as my endpoint contains all needed
inferences materialized.

Thanks!

Below is the configuration file. I'm sorry, but I can't share the sparql
endpoint, because it's hosted on our intranet. Besides this, I changed the
script *cli* to increase Xmmx to 25000M.


prefixes = [ ("of","http://cipe.accamargo.org.br/ontologias/ontofamily.owl#")
 ]


// SPARQL options
sparql.type = "SPARQL endpoint fragment"
sparql.url = "http://192.18.0.125:8890/sparql"
sparql.defaultGraphURIs = {""}
sparql.recursionDepth = 4

//TODOREFACTOR check if predefinedFilter works at all
//predefined filter (1 = YAGO based learning)
// 2 = SKOS, more Options are needed then though. replacePredicate,
breakSuperClassRetrievalAfter
sparql.predefinedFilter = "YAGO"

// the set of objects as starting point for fragment selection
// (should be identical to the set of examples)
sparql.instances = {
"http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente7166750", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente7166750", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente8057650", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente10741180", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente7007779", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente7007779",
"http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente50024160", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente8063290", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente10180430", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente6049338", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente10119100", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente10119120", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente10170120"
}

reasoner.type = "fast instance checker"
reasoner.sources = {sparql}

lp.type = "posNegStandard"
lp.positiveExamples = {
"http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente7166750", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente7166750", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente8057650", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente10741180", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente7007779", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente7007779"
}
lp.negativeExamples = {
"http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente50024160", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente8063290", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente10180430", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente6049338", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente10119100", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente10119120", "
http://cipe.accamargo.org.br/ontologias/recruit.owl#paciente10170120"
}
lp.reasoner = reasoner

alg.type = "celoe"


---
below the exception:


Exception encountered during context initialization - cancelling refresh
attempt
org.springframework.beans.factory.BeanCreationException: Error creating
bean with name 'alg': Injection of autowired dependencies failed; nested
exception is org.springframework.beans.fa
ctory.BeanCreationException: Could not autowire method: public void
org.dllearner.core.AbstractCELA.setReasoner(org.dllearner.core.AbstractReasonerComponent);
nested exception is org.spr
ingframework.beans.factory.BeanCreationException: Error creating bean with
name 'reasoner': Cannot resolve reference to bean 'sparql' while setting
bean property 'sources' with key [0];
nested exception is
org.springframework.beans.factory.BeanCreationException: Error creating
bean with name 'sparql': Initialization of bean failed; nested exception is
java.lang.OutOfMem
oryError: Java heap space
        at
org.springframework.beans.factory.annotation.AutowiredAnnotationBeanPostProcessor.postProcessPropertyValues(AutowiredAnnotationBeanPostProcessor.java:298)
        at
org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.populateBean(AbstractAutowireCapableBeanFactory.java:1148)
        at
org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.doCreateBean(AbstractAutowireCapableBeanFactory.java:519)
        at
org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.createBean(AbstractAutowireCapableBeanFactory.java:458)
        at
org.springframework.beans.factory.support.AbstractBeanFactory$1.getObject(AbstractBeanFactory.java:293)
        at
org.springframework.beans.factory.support.DefaultSingletonBeanRegistry.getSingleton(DefaultSingletonBeanRegistry.java:223)
        at
org.springframework.beans.factory.support.AbstractBeanFactory.doGetBean(AbstractBeanFactory.java:290)
        at
org.springframework.beans.factory.support.AbstractBeanFactory.getBean(AbstractBeanFactory.java:191)
        at
org.springframework.beans.factory.support.DefaultListableBeanFactory.preInstantiateSingletons(DefaultListableBeanFactory.java:636)
        at
org.springframework.context.support.AbstractApplicationContext.finishBeanFactoryInitialization(AbstractApplicationContext.java:934)
        at
org.springframework.context.support.AbstractApplicationContext.refresh(AbstractApplicationContext.java:479)
        at
org.dllearner.configuration.spring.DefaultApplicationContextBuilder.buildApplicationContext(DefaultApplicationContextBuilder.java:60)
        at org.dllearner.cli.CLI.main(CLI.java:259)
Caused by: org.springframework.beans.factory.BeanCreationException: Could
not autowire method: public void
org.dllearner.core.AbstractCELA.setReasoner(org.dllearner.core.AbstractReasonerComponent);
nested exception is
org.springframework.beans.factory.BeanCreationException: Error creating
bean with name 'reasoner': Cannot resolve reference to bean 'sparql' while
setting bean property 'sources' with key [0]; nested exception is
org.springframework.beans.factory.BeanCreationException: Error creating
bean with name 'sparql': Initialization of bean failed; nested exception is
java.lang.OutOfMemoryError: Java heap space
        at
org.springframework.beans.factory.annotation.AutowiredAnnotationBeanPostProcessor$AutowiredMethodElement.inject(AutowiredAnnotationBeanPostProcessor.java:618)
        at
org.springframework.beans.factory.annotation.InjectionMetadata.inject(InjectionMetadata.java:88)
        at
org.springframework.beans.factory.annotation.AutowiredAnnotationBeanPostProcessor.postProcessPropertyValues(AutowiredAnnotationBeanPostProcessor.java:295)
        ... 12 more
Caused by: org.springframework.beans.factory.BeanCreationException: Error
creating bean with name 'reasoner': Cannot resolve reference to bean
'sparql' while setting bean property 'sources' with key [0]; nested
exception is org.springframework.beans.factory.BeanCreationException: Error
creating bean with name 'sparql': Initialization of bean failed; nested
exception is java.lang.OutOfMemoryError: Java heap space
        at
org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveReference(BeanDefinitionValueResolver.java:334)
        at
org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveValueIfNecessary(BeanDefinitionValueResolver.java:108)
        at
org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveManagedSet(BeanDefinitionValueResolver.java:371)
        at
org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveValueIfNecessary(BeanDefinitionValueResolver.java:161)
        at
org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.applyPropertyValues(AbstractAutowireCapableBeanFactory.java:1419)
        at
org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.populateBean(AbstractAutowireCapableBeanFactory.java:1160)
        at
org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.doCreateBean(AbstractAutowireCapableBeanFactory.java:519)
        at
org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.createBean(AbstractAutowireCapableBeanFactory.java:458)
        at
org.springframework.beans.factory.support.AbstractBeanFactory$1.getObject(AbstractBeanFactory.java:293)
        at
org.springframework.beans.factory.support.DefaultSingletonBeanRegistry.getSingleton(DefaultSingletonBeanRegistry.java:223)
        at
org.springframework.beans.factory.support.AbstractBeanFactory.doGetBean(AbstractBeanFactory.java:290)
        at
org.springframework.beans.factory.support.AbstractBeanFactory.getBean(AbstractBeanFactory.java:191)
        at
org.springframework.beans.factory.support.DefaultListableBeanFactory.findAutowireCandidates(DefaultListableBeanFactory.java:921)
        at
org.springframework.beans.factory.support.DefaultListableBeanFactory.doResolveDependency(DefaultListableBeanFactory.java:864)
        at
org.springframework.beans.factory.support.DefaultListableBeanFactory.resolveDependency(DefaultListableBeanFactory.java:779)
        at
org.springframework.beans.factory.annotation.AutowiredAnnotationBeanPostProcessor$AutowiredMethodElement.inject(AutowiredAnnotationBeanPostProcessor.java:575)
        ... 14 more
Caused by: org.springframework.beans.factory.BeanCreationException: Error
creating bean with name 'sparql': Initialization of bean failed; nested
exception is java.lang.OutOfMemoryError: Java heap space
        at
org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.doCreateBean(AbstractAutowireCapableBeanFactory.java:529)
        at
org.springframework.beans.factory.support.AbstractAutowireCapableBeanFactory.createBean(AbstractAutowireCapableBeanFactory.java:458)
        at
org.springframework.beans.factory.support.AbstractBeanFactory$1.getObject(AbstractBeanFactory.java:293)
        at
org.springframework.beans.factory.support.DefaultSingletonBeanRegistry.getSingleton(DefaultSingletonBeanRegistry.java:223)
        at
org.springframework.beans.factory.support.AbstractBeanFactory.doGetBean(AbstractBeanFactory.java:290)
        at
org.springframework.beans.factory.support.AbstractBeanFactory.getBean(AbstractBeanFactory.java:191)
        at
org.springframework.beans.factory.support.BeanDefinitionValueResolver.resolveReference(BeanDefinitionValueResolver.java:328)
        ... 29 more
Caused by: java.lang.OutOfMemoryError: Java heap space
        at java.util.Arrays.copyOfRange(Arrays.java:2694)
        at java.lang.String.<init>(String.java:234)
        at java.lang.StringBuilder.toString(StringBuilder.java:405)
        at
org.apache.jena.atlas.json.io.parser.TokenizerJSON.allBetween(TokenizerJSON.java:575)
        at
org.apache.jena.atlas.json.io.parser.TokenizerJSON.parseToken(TokenizerJSON.java:137)
        at
org.apache.jena.atlas.json.io.parser.TokenizerJSON.hasNext(TokenizerJSON.java:59)
        at
org.apache.jena.atlas.iterator.PeekIterator.fill(PeekIterator.java:50)
        at
org.apache.jena.atlas.iterator.PeekIterator.next(PeekIterator.java:92)
        at
org.apache.jena.atlas.json.io.parser.JSONParserBase.nextToken(JSONParserBase.java:107)
        at
org.apache.jena.atlas.json.io.parser.JSONP.parseObject(JSONP.java:75)
        at
org.apache.jena.atlas.json.io.parser.JSONP.parseAny(JSONP.java:97)
        at
org.apache.jena.atlas.json.io.parser.JSONP.parseObject(JSONP.java:79)
        at
org.apache.jena.atlas.json.io.parser.JSONP.parseAny(JSONP.java:97)
        at
org.apache.jena.atlas.json.io.parser.JSONP.parseArray(JSONP.java:146)
        at
org.apache.jena.atlas.json.io.parser.JSONP.parseAny(JSONP.java:98)
        at
org.apache.jena.atlas.json.io.parser.JSONP.parseObject(JSONP.java:79)
        at
org.apache.jena.atlas.json.io.parser.JSONP.parseAny(JSONP.java:97)
        at
org.apache.jena.atlas.json.io.parser.JSONP.parseObject(JSONP.java:79)
        at org.apache.jena.atlas.json.io.parser.JSONP.parse(JSONP.java:50)
        at
org.apache.jena.atlas.json.io.parser.JSONParser.parse(JSONParser.java:58)
        at
org.apache.jena.atlas.json.io.parser.JSONParser.parse(JSONParser.java:40)
        at org.apache.jena.atlas.json.JSON._parse(JSON.java:141)
        at org.apache.jena.atlas.json.JSON.parse(JSON.java:37)
        at
com.hp.hpl.jena.sparql.resultset.JSONInput.parse(JSONInput.java:125)
        at
com.hp.hpl.jena.sparql.resultset.JSONInput.process(JSONInput.java:109)
        at
com.hp.hpl.jena.sparql.resultset.JSONInput.fromJSON(JSONInput.java:66)
        at
com.hp.hpl.jena.query.ResultSetFactory.fromJSON(ResultSetFactory.java:346)
        at
org.dllearner.kb.sparql.SparqlQuery.convertJSONtoResultSet(SparqlQuery.java:300)
        at
org.dllearner.kb.sparql.SPARQLTasks.queryAsRDFNodeTuple(SPARQLTasks.java:413)
        at
org.dllearner.kb.aquisitors.SparqlTupleAquisitor.retrieveTupel(SparqlTupleAquisitor.java:70)
        at
org.dllearner.kb.aquisitors.TupleAquisitor.getTupelForResource(TupleAquisitor.java:65)
        at
org.dllearner.kb.extraction.InstanceNode.expand(InstanceNode.java:68)
An Error Has Occurred During Processing.
Terminating DL-Learner...and writing stacktrace to: log/error.log




--
diogo patrão



On Thu, Feb 19, 2015 at 9:25 AM, Lorenz Bühmann <
spo...@st...> wrote:

>  Hello Diogo,
>
> thanks a lot.
>
> Can you share the conf file with us and maybe give us access to the
> endpoint or share the dump? Recursion depth 4 usually leads to a large
> amount of imported data for each given pos/neg example so there might be
> indeed some memory issue. Do you get just an OOM exception or something
> else? can you share the error stack trace?
>
> 1) Actually, it's not possible to configure a black or white list of
> allowed properties, but we could add this of course.
>
> 2) I'm not sure what you exactly mean by that. Do you mean the
> initialization step of the reasoner before the learning algorithm is
> started?
>
> Kind regards,
> Lorenz
>
>  Hello
>
>  First, congratulations on the release of this exciting project!
>
>  I have a knowledge base about cancer patients with 6.240.880 triples,
> loaded on a open source virtuoso server. Before loading, we materialized
> inferences using pellet - it took almost 3 days in a 30GB RAM server.
>
>  I have a configuration dl-learner file (based on the Actors example)
> which queries the SPARQL endpoint. But as I set the recursion level to 4,
> dl-learner end up taking too much memory and throws and exception.
>
>  I have two questions:
>
>  1) Is it possible to configure what would be queried by dl-learner?
> There are data and object properties which could be removed from the
> analysis. Is there a way to filter it?
>
>  2) Can I turn off the built-in inference provided by dl-learner?
> (considering that everything is materialized in my KB).
>
>  Thanks!
>
>  --
> diogo patrão
>
>
>
>
> ------------------------------------------------------------------------------
> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
> from Actuate! Instantly Supercharge Your Business Reports and Dashboards
> with Interactivity, Sharing, Native Excel Exports, App Integration & more
> Get technology previously reserved for billion-dollar corporations, FREEhttp://pubads.g.doubleclick.net/gampad/clk?id=190641631&iu=/4140/ostg.clktrk
>
>
>
> _______________________________________________
> dl-learner-discussion mailing lis...@li...://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>
>
>
>
> ------------------------------------------------------------------------------
> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
> from Actuate! Instantly Supercharge Your Business Reports and Dashboards
> with Interactivity, Sharing, Native Excel Exports, App Integration & more
> Get technology previously reserved for billion-dollar corporations, FREE
>
> http://pubads.g.doubleclick.net/gampad/clk?id=190641631&iu=/4140/ostg.clktrk
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>
>

Re: [DL-Learner discussion] tips on large knowledge bases

[Lorenz B. <spo...@st...>]

Hello Diogo,

thanks a lot.

Can you share the conf file with us and maybe give us access to the 
endpoint or share the dump? Recursion depth 4 usually leads to a large 
amount of imported data for each given pos/neg example so there might be 
indeed some memory issue. Do you get just an OOM exception or something 
else? can you share the error stack trace?

1) Actually, it's not possible to configure a black or white list of 
allowed properties, but we could add this of course.

2) I'm not sure what you exactly mean by that. Do you mean the 
initialization step of the reasoner before the learning algorithm is 
started?

Kind regards,
Lorenz

> Hello
>
> First, congratulations on the release of this exciting project!
>
> I have a knowledge base about cancer patients with 6.240.880 triples, 
> loaded on a open source virtuoso server. Before loading, we 
> materialized inferences using pellet - it took almost 3 days in a 30GB 
> RAM server.
>
> I have a configuration dl-learner file (based on the Actors example) 
> which queries the SPARQL endpoint. But as I set the recursion level to 
> 4, dl-learner end up taking too much memory and throws and exception.
>
> I have two questions:
>
> 1) Is it possible to configure what would be queried by dl-learner? 
> There are data and object properties which could be removed from the 
> analysis. Is there a way to filter it?
>
> 2) Can I turn off the built-in inference provided by dl-learner? 
> (considering that everything is materialized in my KB).
>
> Thanks!
>
> -- 
> diogo patrão
>
>
>
>
> ------------------------------------------------------------------------------
> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
> from Actuate! Instantly Supercharge Your Business Reports and Dashboards
> with Interactivity, Sharing, Native Excel Exports, App Integration & more
> Get technology previously reserved for billion-dollar corporations, FREE
> http://pubads.g.doubleclick.net/gampad/clk?id=190641631&iu=/4140/ostg.clktrk
>
>
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

[DL-Learner discussion] tips on large knowledge bases

[Diogo FC P. <djo...@gm...>]

Hello

First, congratulations on the release of this exciting project!

I have a knowledge base about cancer patients with 6.240.880 triples,
loaded on a open source virtuoso server. Before loading, we materialized
inferences using pellet - it took almost 3 days in a 30GB RAM server.

I have a configuration dl-learner file (based on the Actors example) which
queries the SPARQL endpoint. But as I set the recursion level to 4,
dl-learner end up taking too much memory and throws and exception.

I have two questions:

1) Is it possible to configure what would be queried by dl-learner? There
are data and object properties which could be removed from the analysis. Is
there a way to filter it?

2) Can I turn off the built-in inference provided by dl-learner?
(considering that everything is materialized in my KB).

Thanks!

--
diogo patrão

[DL-Learner discussion] DL-Learner 1.0 (Supervised Structured Machine Learning Framework) Released

[Jens L. <le...@in...>]

Dear all,

the AKSW group [1] is happy to announce DL-Learner 1.0.

DL-Learner is a framework containing algorithms for supervised machine 
learning in RDF and OWL. DL-Learner can use various RDF and OWL 
serialization formats as well as SPARQL endpoints as input, can connect 
to most popular OWL reasoners and is easily and flexibly configurable. 
It extends concepts of Inductive Logic Programming and Relational 
Learning to the Semantic Web in order to allow powerful data analysis.

Website: http://dl-learner.org
GitHub page: https://github.com/AKSW/DL-Learner
Download: https://github.com/AKSW/DL-Learner/releases
ChangeLog: http://dl-learner.org/development/changelog/

DL-Learner is used for data analysis tasks within other tools such as 
ORE [2] and RDFUnit [3]. Technically, it uses refinement operator based, 
pattern based and evolutionary techniques for learning on structured 
data. For a practical example, see [4]. DL-Learner also offers a plugin 
for Protégé [5], which can give suggestions for axioms to add. 
DL-Learner is part of the Linked Data Stack [6] - a repository for 
Linked Data management tools.

We want to thank everyone who helped to create this release, in 
particular (alphabetically) An Tran, Chris Shellenbarger, Christoph 
Haase, Daniel Fleischhacker, Didier Cherix, Johanna Völker, Konrad 
Höffner, Robert Höhndorf, Sebastian Hellmann and Simon Bin. We also 
acknowledge support by the recently started SAKE project, in which 
DL-Learner will be applied to event analysis in manufacturing use cases, 
as well as the GeoKnow [7] and Big Data Europe [8] projects where it is 
part of the respective platforms.

View this announcement on Twitter and the AKSW blog:
   https://twitter.com/dllearner/status/566172443442958336
   http://blog.aksw.org/2015/dl-learner-1-0/

Kind regards,

Lorenz Bühmann, Jens Lehmann and Patrick Westphal

[1] http://aksw.org
[2] http://ore-tool.net
[3] http://aksw.org/Projects/RDFUnit.html
[4] http://dl-learner.org/community/carcinogenesis/
[5] https://github.com/AKSW/DL-Learner-Protege-Plugin
[6] http://stack.linkeddata.org
[7] http://geoknow.eu
[8] http://www.big-data-europe.eu

-- 
Dr. Jens Lehmann
Head of AKSW group, University of Leipzig
Homepage: http://www.jens-lehmann.org
Group: http://aksw.org - semantic web research center
Project: http://geoknow.eu - geospatial data on the web

Re: [DL-Learner discussion] dl-learner and noise

[Lorenz B. <spo...@st...>]

Hello Céline,

there is no need to apologize. This is the official DL-Learner 
discussion list and we're always happy when we can help people in using 
DL-Learner or answer questions.

1. Yes, at least for the learning algorithm CELOE there is a parameter 
called "maxNrOfResults" which limits the number of returned solutions. 
I'm not sure if I understand the second point, do you ask for another 
option that handles all solution with the same accuracy as one solution, 
thus, you want e.g. n distinct solutions with different score?

2. The solutions are sorted by:

accuracy > length > class expression type

We use the length as second criteria because of readability and 
simplicity, as you already assumed. Indeed there might be other sorting 
priorities (probably depending on the use-case and/or dataset), but the 
main focus is on the accuracy. If you have any other in mind, let us 
know and we will think about it.


“hasCulture min 6 Culture” is not in the solution list, because it's 
follows logically from the knowledge base itself. We have an optional 
parameter that rewrites the class expressions exactly like in your 
example, but that's unfortunately not yet available in the conf files. 
I've open a feature request [1].

3. You're right. This is not very efficient and makes the results more 
confusing. I wouldn't say that this is a bug, but definitely needs to be 
avoided. I opened a ticket [2].


Kind regards,
Lorenz


[1] https://github.com/AKSW/DL-Learner/issues/2
[2] https://github.com/AKSW/DL-Learner/issues/3

> Hello Lorenz,
>
> Thank you again for your answer. I wish you a happy new year. I am sorry
> to disturb you again with some new questions…
>
> 1. Is there a parameter to display more than the 10 first best
> solutions? For example, to display all the solutions with the same
> accuracy and f-measure as the first solution.
>
>
> 2. What is the criteria for the order of the answers (besides accuracy
> and f-measure)?
> If there are several solutions with the same accuracy/f-measure, how do
> you choose what solution to put in the first position?
> I think it is related to the readability, but could you explain a bit
> more please?
>
> I am asking this because I am confused with 2 things:
>
> - If I take the “hasCulture min 6 Thing” with the parameters you have
> added: the second solution is “hasCulture min 6 (not WaterBody)” and the
> next ones are all like “hasCulture min 6 (not Concept_Name)”. I don’t
> understand why “hasCulture min 6 Culture” is not at least in the second
> position. I mean, “Culture” is more readable than “(not Concept_name)”,
> isn’t it?
>
> - Moreover, in this context, “hasCulture min 6 Culture” and “hasCulture
> min 6 Thing” are equivalent because Culture is the range of hasCulture.
> But in my opinion, it would be more adapted to get “hasCulture min 6
> Culture” before “hasCulture min 6 Thing”. I mean, I would rather say to
> an expert that a cultural destination is a destination which hasCulture
> at least “6 instances of Culture” rather than “6 instances of Thing”. Of
> course, this is only a matter of readability for an expert, since saying
> “Thing” or “Culture” does not change anything if the ontology is
> consistent.
>
>
> 3. Is there a way to avoid to get solutions with the same
> accuracy/f-measure as one solution but which are much more complex for
> no reason?
>
> For example, if I disable the “not” and “only” with the “hasCulture min
> 6 Thing” example, I get:
> 1: hasCulture min 6 Thing
> 2: Destination and hasCulture min 6 Thing
> 3: hasCulture min 6 Thing and (Destination or Weather)
> 4: hasCulture min 6 Thing and (Destination or Season)
> Etc
>
> All of these definitions are 100% correct.
> But what is the point to get the “or Weather” / “or Season” part? If I
> say “hasCulture min 6 Thing and (Destination or ConceptA or ConceptB or
> …)”, of course I can add anything with “or”, it would not change the
> accuracy but I don’t see the point.
>
>
> Thanks a lot,
> Céline
>
>
> ------------------------------------------------------------------------------
> Dive into the World of Parallel Programming! The Go Parallel Website,
> sponsored by Intel and developed in partnership with Slashdot Media, is your
> hub for all things parallel software development, from weekly thought
> leadership blogs to news, videos, case studies, tutorials and more. Take a
> look and join the conversation now. http://goparallel.sourceforge.net
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

Re: [DL-Learner discussion] dl-learner and noise

[alec <Cel...@lr...>]

Hello Lorenz,

Thank you again for your answer. I wish you a happy new year. I am sorry 
to disturb you again with some new questions…

1. Is there a parameter to display more than the 10 first best 
solutions? For example, to display all the solutions with the same 
accuracy and f-measure as the first solution.


2. What is the criteria for the order of the answers (besides accuracy 
and f-measure)?
If there are several solutions with the same accuracy/f-measure, how do 
you choose what solution to put in the first position?
I think it is related to the readability, but could you explain a bit 
more please?

I am asking this because I am confused with 2 things:

- If I take the “hasCulture min 6 Thing” with the parameters you have 
added: the second solution is “hasCulture min 6 (not WaterBody)” and the 
next ones are all like “hasCulture min 6 (not Concept_Name)”. I don’t 
understand why “hasCulture min 6 Culture” is not at least in the second 
position. I mean, “Culture” is more readable than “(not Concept_name)”, 
isn’t it?

- Moreover, in this context, “hasCulture min 6 Culture” and “hasCulture 
min 6 Thing” are equivalent because Culture is the range of hasCulture. 
But in my opinion, it would be more adapted to get “hasCulture min 6 
Culture” before “hasCulture min 6 Thing”. I mean, I would rather say to 
an expert that a cultural destination is a destination which hasCulture 
at least “6 instances of Culture” rather than “6 instances of Thing”. Of 
course, this is only a matter of readability for an expert, since saying 
“Thing” or “Culture” does not change anything if the ontology is 
consistent.


3. Is there a way to avoid to get solutions with the same 
accuracy/f-measure as one solution but which are much more complex for 
no reason?

For example, if I disable the “not” and “only” with the “hasCulture min 
6 Thing” example, I get:
1: hasCulture min 6 Thing
2: Destination and hasCulture min 6 Thing
3: hasCulture min 6 Thing and (Destination or Weather)
4: hasCulture min 6 Thing and (Destination or Season)
Etc

All of these definitions are 100% correct.
But what is the point to get the “or Weather” / “or Season” part? If I 
say “hasCulture min 6 Thing and (Destination or ConceptA or ConceptB or 
…)”, of course I can add anything with “or”, it would not change the 
accuracy but I don’t see the point.


Thanks a lot,
Céline

Re: [DL-Learner discussion] dl-learner and noise

[alec <Cel...@lr...>]

Hello Lorenz,

Thank you very much for all the time you spent on this! This is very 
helpful.

Best regards,
Céline


Le 29-12-2014 16:03, Lorenz Bühmann a écrit :
> Hello Céline,
> 
>  let me try to explain why things do not work as you would expect.
> 
>  First of all, please always keep in mind that DL-Learner is searching
> for OWL class expressions that
> 
>  1. cover as many positive examples as possible
>  2. cover as less negative examples as possible
> 
>  This search space is quite large and that's why, assuming that a
> perfect solution exists (this is not always the case despite the
> trivial solution), it might be the case that DL-Learner needs much
> more time than the default 10 seconds.
> 
>  According to your examples:
> 
>  1. has6culture.conf
>  There is a default limit of n=5 for cardinalities, that's why your
> solution is never tested. We are working on a more intelligent
> solution. For now, you can increase the limit by
> 
>  op.type = "rho"
>  op.cardinalityLimit = 10
> 
>  Based on the latest source code, I get
> 
>  solutions:
>  1: hasCulture min 6 Thing (pred. acc.: 100.00%, F-measure: 100.00%)
>  2: hasCulture min 6 (not WaterBody) (pred. acc.: 100.00%, F-measure:
> 100.00%)
>  3: hasCulture min 6 (not Volcano) (pred. acc.: 100.00%, F-measure:
> 100.00%)
>  4: hasCulture min 6 (not View) (pred. acc.: 100.00%, F-measure:
> 100.00%)
>  5: hasCulture min 6 (not Urban) (pred. acc.: 100.00%, F-measure:
> 100.00%)
>  6: hasCulture min 6 (not TriumphalArch) (pred. acc.: 100.00%,
> F-measure: 100.00%)
>  7: hasCulture min 6 (not Tower) (pred. acc.: 100.00%, F-measure:
> 100.00%)
>  8: hasCulture min 6 (not Stadium) (pred. acc.: 100.00%, F-measure:
> 100.00%)
>  9: hasCulture min 6 (not QualityEnvironment) (pred. acc.: 100.00%,
> F-measure: 100.00%)
>  10: hasCulture min 6 (not Amphitheatre) (pred. acc.: 100.00%,
> F-measure: 100.00%)
> 
>  2. hasOldTownOrShopping.conf
>  You have to increase the runtime.
> 
>  alg.maxExecutionTimeInSeconds = 600
> 
>  Moreover, it could help to disable some OWL constructs like negation
> and universal restrictions.
>  op.type = "rho"
>  op.useNegation = false //disable negation (not)
>  op.useAllConstructor = false //disable universal restrictions (only)
> 
>  The output is
> 
>  solutions:
>  1: (hasActivity some OldTown or hasEnvironment some Shopping) (pred.
> acc.: 100.00%, F-measure: 100.00%)
>  2: (hasActivity some OldTown or hasEnvironment some Urban) (pred.
> acc.: 97.50%, F-measure: 98.04%)
>  3: (hasActivity some Promenade or hasEnvironment some Urban) (pred.
> acc.: 92.50%, F-measure: 94.34%)
>  4: (hasActivity some Promenade or hasEnvironment some Shopping)
> (pred. acc.: 92.50%, F-measure: 94.34%)
>  5: hasActivity min 2 (Excursion or Lazing) (pred. acc.: 70.00%,
> F-measure: 80.65%)
>  6: hasActivity min 2 (Excursion or WaterActivity) (pred. acc.:
> 68.75%, F-measure: 80.00%)
>  7: hasActivity min 2 (Excursion or Relaxation) (pred. acc.: 68.75%,
> F-measure: 80.00%)
>  8: hasActivity min 2 (Excursion or Nightlife) (pred. acc.: 68.75%,
> F-measure: 80.00%)
>  9: hasActivity min 2 (Excursion or NaturalEnvironment) (pred. acc.:
> 68.75%, F-measure: 80.00%)
>  10: hasActivity min 2 (Environment or Excursion) (pred. acc.: 68.75%,
> F-measure: 80.00%)
>   
> 
>  3. hasBathingMidSummer.conf
>  The same holds for your 3rd example - this is really complex and far
> from easy to learn. Increase the runtime and disable some OWL
> constructs. Additionally, if you assume to get longer descriptions,
> you can set a parameter for the search heuristic like
> 
>  h.type ="celoe_heuristic"
>  h.expansionPenaltyFactor = 0.02
> 
>  The algorithm is still running and the output so far is
> 
>  (hasEnvironment some Bathing and hasWeather some (avgTemperatureC
> some double[>= 21.8563] and precipitationMm some double[<= 33.65835]
> and concernMonth some hasSeason some MidSummer))
> 
>  which was found after 1min 43s 32ms.
> 
>  I attached all config files.
> 
>  Kind regards,
>  Lorenz
> 
>> Hello Lorenz,
>> 
>> Thanks a lot, that helps.
>> 
>> I have another problem. I have an ontology describing holiday’s
>> destinations. If I want to learn some simple definitions like
>> "hasActivity some Animation" (or any "hasObjectProperty some
>> Concept") it works well. But if my definitions are more complicated,
>> it does not work. There is no noise in my examples.
>> 
>> I want to learn:
>> 1. "hasCulture min 6 Culture"
>> 2. "(hasActivity some OldTown) or (hasEnvironment some Shopping)"
>> 3. "(has Activity some Bathing) and (hasWeather min 2 (
>> (avgTemperature some double [>=23.0]) and (precipitationMm some
>> double[<=70.0]) and (concernMonth some hasSeason some MidSummer)))"
>> 
>> I get:
>> 
>> 1. hasCulture min 5 ((not Archaeology) and (not Architecture))
>> (pred. acc.: 97,50%, F-measure: 94,74%)
>> 2. hasActivity only (not TowedWatersport) (pred. acc.: 68,75%,
>> F-measure: 80,00%)
>> 3. (hasActivity some Excursion and hasEnvironment some
>> WaterActivity) (pred. acc.: 68,75%, F-measure: 73,12%)
>> 
>> Could you explain why I do not get the good definitions (or at
>> least some definitions with 100% of accuracy)? Are my definitions
>> too complicated? Did I miss something?
>> Thank you in advance! Sorry for disturbing you again.
>> 
>> Best regards,
>> Céline
>> 
>> Le 18-12-2014 18:40, Lorenz Bühmann a écrit :
>> Hello Céline,
>> 
>>  there is a noise parameter called "noisePercentage" for the
>> learning
>> algorithm CELOE, so you could simply define a noise value like 20%.
>> 
>> This will allow to return solutions in which 20% of the positive
>> examples are not covered by the solution.
>> 
>>  Your conf file should be extended by the line:
>> 
>>  alg.noisePercentage = "20"
>> 
>>  Hope this helps.
>> 
>>  Kind regards,
>>  Lorenz
>> 
>> Hello,
>> 
>> I have an ontology describing 10000 films. I have built a conf file
>> 
>> with positive and negative examples for “American films”. I
>> want
>> to learn that an American film “isFromCountry some _US”. When I
>> 
>> run DL-Learner, it works well. Now, if I add a little bit of noise
>> (changing one positive example into negative example and vice
>> versa), it does not work anymore. “isFromCountry some Country”
>> is learned (acc=42.86%). I have 10000 films, so this noise
>> represents only 0.02% of error.
>> Is it possible to get the good definition with some noise in my
>> examples by running DL-Learner with some parameters I don’t know
>> about or does DL-Learner just not handle noise?
>> 
>> Thanks in advance for your answer. I attached the files if you want
>> 
>> to make some tests. I use dllearner-1.0-beta-3 on Windows.
>> 
>> Best regards,
>> Céline
>> 
>> 
> ------------------------------------------------------------------------------
>> 
>> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
>> from Actuate! Instantly Supercharge Your Business Reports and
>> Dashboards
>> with Interactivity, Sharing, Native Excel Exports, App Integration
>> &
>> more
>> Get technology previously reserved for billion-dollar corporations,
>> 
>> FREE
>> 
>> 
> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk
>> [1]
>> [1]
>> 
>> _______________________________________________
>> dl-learner-discussion mailing list
>> dl-...@li...
>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>> [2]
>> [2]
>> 
>> Links:
>> ------
>> [1]
>> 
> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&amp;iu=/4140/ostg.clktrk
>> [3]
>> [2]
>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>> [2]
>> 
>> 
> ------------------------------------------------------------------------------
>> 
>> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
>> from Actuate! Instantly Supercharge Your Business Reports and
>> Dashboards
>> with Interactivity, Sharing, Native Excel Exports, App Integration
>> & more
>> Get technology previously reserved for billion-dollar corporations,
>> FREE
>> 
> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk
>> [1]
>> 
>> _______________________________________________
>> dl-learner-discussion mailing list
>> dl-...@li...
>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>> [2]
> 
> ------------------------------------------------------------------------------
> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
> from Actuate! Instantly Supercharge Your Business Reports and
> Dashboards
> with Interactivity, Sharing, Native Excel Exports, App Integration &
> more
> Get technology previously reserved for billion-dollar corporations,
> FREE
> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk
> [1]
> 
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion [2]
> 
> 
> 
> Links:
> ------
> [1]
> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&amp;iu=/4140/ostg.clktrk
> [2] https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
> [3]
> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&amp;amp;iu=/4140/ostg.clktrk
> 
> ------------------------------------------------------------------------------
> Dive into the World of Parallel Programming! The Go Parallel Website,
> sponsored by Intel and developed in partnership with Slashdot Media, is 
> your
> hub for all things parallel software development, from weekly thought
> leadership blogs to news, videos, case studies, tutorials and more. 
> Take a
> look and join the conversation now. http://goparallel.sourceforge.net
> 
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

Re: [DL-Learner discussion] dl-learner and noise

[Lorenz B. <spo...@st...>]

Hello Céline,

let me try to explain why things do not work as you would expect.

First of all, please always keep in mind that DL-Learner is searching 
for OWL class expressions that

1. cover as many positive examples as possible
2. cover as less negative examples as possible

This search space is quite large and that's why, assuming that a perfect 
solution exists (this is not always the case despite the trivial 
solution), it might be the case that DL-Learner needs much more time 
than the default 10 seconds.

According to your examples:

1. has6culture.conf
There is a default limit of n=5 for cardinalities, that's why your 
solution is never tested. We are working on a more intelligent solution. 
For now, you can increase the limit by

op.type = "rho"
op.cardinalityLimit = 10

Based on the latest source code, I get

solutions:
1: hasCulture min 6 Thing (pred. acc.: 100.00%, F-measure: 100.00%)
2: hasCulture min 6 (not WaterBody) (pred. acc.: 100.00%, F-measure: 
100.00%)
3: hasCulture min 6 (not Volcano) (pred. acc.: 100.00%, F-measure: 100.00%)
4: hasCulture min 6 (not View) (pred. acc.: 100.00%, F-measure: 100.00%)
5: hasCulture min 6 (not Urban) (pred. acc.: 100.00%, F-measure: 100.00%)
6: hasCulture min 6 (not TriumphalArch) (pred. acc.: 100.00%, F-measure: 
100.00%)
7: hasCulture min 6 (not Tower) (pred. acc.: 100.00%, F-measure: 100.00%)
8: hasCulture min 6 (not Stadium) (pred. acc.: 100.00%, F-measure: 100.00%)
9: hasCulture min 6 (not QualityEnvironment) (pred. acc.: 100.00%, 
F-measure: 100.00%)
10: hasCulture min 6 (not Amphitheatre) (pred. acc.: 100.00%, F-measure: 
100.00%)


2. hasOldTownOrShopping.conf
You have to increase the runtime.

alg.maxExecutionTimeInSeconds = 600

Moreover, it could help to disable some OWL constructs like negation and 
universal restrictions.
op.type = "rho"
op.useNegation = false //disable negation (not)
op.useAllConstructor = false //disable universal restrictions (only)

The output is

solutions:
1: (hasActivity some OldTown or hasEnvironment some Shopping) (pred. 
acc.: 100.00%, F-measure: 100.00%)
2: (hasActivity some OldTown or hasEnvironment some Urban) (pred. acc.: 
97.50%, F-measure: 98.04%)
3: (hasActivity some Promenade or hasEnvironment some Urban) (pred. 
acc.: 92.50%, F-measure: 94.34%)
4: (hasActivity some Promenade or hasEnvironment some Shopping) (pred. 
acc.: 92.50%, F-measure: 94.34%)
5: hasActivity min 2 (Excursion or Lazing) (pred. acc.: 70.00%, 
F-measure: 80.65%)
6: hasActivity min 2 (Excursion or WaterActivity) (pred. acc.: 68.75%, 
F-measure: 80.00%)
7: hasActivity min 2 (Excursion or Relaxation) (pred. acc.: 68.75%, 
F-measure: 80.00%)
8: hasActivity min 2 (Excursion or Nightlife) (pred. acc.: 68.75%, 
F-measure: 80.00%)
9: hasActivity min 2 (Excursion or NaturalEnvironment) (pred. acc.: 
68.75%, F-measure: 80.00%)
10: hasActivity min 2 (Environment or Excursion) (pred. acc.: 68.75%, 
F-measure: 80.00%)


3. hasBathingMidSummer.conf
The same holds for your 3rd example - this is really complex and far 
from easy to learn. Increase the runtime and disable some OWL 
constructs. Additionally, if you assume to get longer descriptions, you 
can set a parameter for the search heuristic like

h.type ="celoe_heuristic"
h.expansionPenaltyFactor = 0.02

The algorithm is still running and the output so far is

(hasEnvironment some Bathing and hasWeather some (avgTemperatureC some 
double[>= 21.8563] and precipitationMm some double[<= 33.65835] and 
concernMonth some hasSeason some MidSummer))

which was found after 1min 43s 32ms.



I attached all config files.

Kind regards,
Lorenz

> Hello Lorenz,
>
> Thanks a lot, that helps.
>
> I have another problem. I have an ontology describing holiday’s 
> destinations. If I want to learn some simple definitions like 
> "hasActivity some Animation" (or any "hasObjectProperty some Concept") 
> it works well. But if my definitions are more complicated, it does not 
> work. There is no noise in my examples.
>
> I want to learn:
> 1. "hasCulture min 6 Culture"
> 2. "(hasActivity some OldTown) or (hasEnvironment some Shopping)"
> 3. "(has Activity some Bathing) and (hasWeather min 2 ( 
> (avgTemperature some double [>=23.0]) and (precipitationMm some 
> double[<=70.0]) and (concernMonth some hasSeason some MidSummer)))"
>
> I get:
>
> 1. hasCulture min 5 ((not Archaeology) and (not Architecture)) (pred. 
> acc.: 97,50%, F-measure: 94,74%)
> 2. hasActivity only (not TowedWatersport) (pred. acc.: 68,75%, 
> F-measure: 80,00%)
> 3. (hasActivity some Excursion and hasEnvironment some WaterActivity) 
> (pred. acc.: 68,75%, F-measure: 73,12%)
>
> Could you explain why I do not get the good definitions (or at least 
> some definitions with 100% of accuracy)? Are my definitions too 
> complicated? Did I miss something?
> Thank you in advance! Sorry for disturbing you again.
>
> Best regards,
> Céline
>
>
>
>
> Le 18-12-2014 18:40, Lorenz Bühmann a écrit :
>> Hello Céline,
>>
>>  there is a noise parameter called "noisePercentage" for the learning
>> algorithm CELOE, so you could simply define a noise value like 20%.
>> This will allow to return solutions in which 20% of the positive
>> examples are not covered by the solution.
>>
>>  Your conf file should be extended by the line:
>>
>>  alg.noisePercentage = "20"
>>
>>  Hope this helps.
>>
>>  Kind regards,
>>  Lorenz
>>
>>> Hello,
>>>
>>> I have an ontology describing 10000 films. I have built a conf file
>>> with positive and negative examples for “American films”. I want
>>> to learn that an American film “isFromCountry some _US”. When I
>>> run DL-Learner, it works well. Now, if I add a little bit of noise
>>> (changing one positive example into negative example and vice
>>> versa), it does not work anymore. “isFromCountry some Country”
>>> is learned (acc=42.86%). I have 10000 films, so this noise
>>> represents only 0.02% of error.
>>> Is it possible to get the good definition with some noise in my
>>> examples by running DL-Learner with some parameters I don’t know
>>> about or does DL-Learner just not handle noise?
>>>
>>> Thanks in advance for your answer. I attached the files if you want
>>> to make some tests. I use dllearner-1.0-beta-3 on Windows.
>>>
>>> Best regards,
>>> Céline
>>>
>>>
>> ------------------------------------------------------------------------------ 
>>
>>> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
>>> from Actuate! Instantly Supercharge Your Business Reports and
>>> Dashboards
>>> with Interactivity, Sharing, Native Excel Exports, App Integration &
>>> more
>>> Get technology previously reserved for billion-dollar corporations,
>>> FREE
>>>
>> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk 
>>
>>> [1]
>>>
>>> _______________________________________________
>>> dl-learner-discussion mailing list
>>> dl-...@li...
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>> [2]
>>
>>
>>
>> Links:
>> ------
>> [1]
>> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&amp;iu=/4140/ostg.clktrk 
>>
>> [2] https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>
>> ------------------------------------------------------------------------------ 
>>
>> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
>> from Actuate! Instantly Supercharge Your Business Reports and Dashboards
>> with Interactivity, Sharing, Native Excel Exports, App Integration & 
>> more
>> Get technology previously reserved for billion-dollar corporations, FREE
>> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk 
>>
>>
>> _______________________________________________
>> dl-learner-discussion mailing list
>> dl-...@li...
>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>
>
> ------------------------------------------------------------------------------
> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
> from Actuate! Instantly Supercharge Your Business Reports and Dashboards
> with Interactivity, Sharing, Native Excel Exports, App Integration & more
> Get technology previously reserved for billion-dollar corporations, FREE
> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk
>
>
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

Re: [DL-Learner discussion] dl-learner and noise

[alec <Cel...@lr...>]

Hello Lorenz,

Thanks a lot, that helps.

I have another problem. I have an ontology describing holiday’s 
destinations. If I want to learn some simple definitions like 
"hasActivity some Animation" (or any "hasObjectProperty some Concept") 
it works well. But if my definitions are more complicated, it does not 
work. There is no noise in my examples.

I want to learn:
1. "hasCulture min 6 Culture"
2. "(hasActivity some OldTown) or (hasEnvironment some Shopping)"
3. "(has Activity some Bathing) and (hasWeather min 2 ( (avgTemperature 
some double [>=23.0]) and (precipitationMm some double[<=70.0]) and 
(concernMonth some hasSeason some MidSummer)))"

I get:

1. hasCulture min 5 ((not Archaeology) and (not Architecture)) (pred. 
acc.: 97,50%, F-measure: 94,74%)
2. hasActivity only (not TowedWatersport) (pred. acc.: 68,75%, 
F-measure: 80,00%)
3. (hasActivity some Excursion and hasEnvironment some WaterActivity) 
(pred. acc.: 68,75%, F-measure: 73,12%)

Could you explain why I do not get the good definitions (or at least 
some definitions with 100% of accuracy)? Are my definitions too 
complicated? Did I miss something?
Thank you in advance! Sorry for disturbing you again.

Best regards,
Céline




Le 18-12-2014 18:40, Lorenz Bühmann a écrit :
> Hello Céline,
> 
>  there is a noise parameter called "noisePercentage" for the learning
> algorithm CELOE, so you could simply define a noise value like 20%.
> This will allow to return solutions in which 20% of the positive
> examples are not covered by the solution.
> 
>  Your conf file should be extended by the line:
> 
>  alg.noisePercentage = "20"
> 
>  Hope this helps.
> 
>  Kind regards,
>  Lorenz
> 
>> Hello,
>> 
>> I have an ontology describing 10000 films. I have built a conf file
>> with positive and negative examples for “American films”. I want
>> to learn that an American film “isFromCountry some _US”. When I
>> run DL-Learner, it works well. Now, if I add a little bit of noise
>> (changing one positive example into negative example and vice
>> versa), it does not work anymore. “isFromCountry some Country”
>> is learned (acc=42.86%). I have 10000 films, so this noise
>> represents only 0.02% of error.
>> Is it possible to get the good definition with some noise in my
>> examples by running DL-Learner with some parameters I don’t know
>> about or does DL-Learner just not handle noise?
>> 
>> Thanks in advance for your answer. I attached the files if you want
>> to make some tests. I use dllearner-1.0-beta-3 on Windows.
>> 
>> Best regards,
>> Céline
>> 
>> 
> ------------------------------------------------------------------------------
>> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
>> from Actuate! Instantly Supercharge Your Business Reports and
>> Dashboards
>> with Interactivity, Sharing, Native Excel Exports, App Integration &
>> more
>> Get technology previously reserved for billion-dollar corporations,
>> FREE
>> 
> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk
>> [1]
>> 
>> _______________________________________________
>> dl-learner-discussion mailing list
>> dl-...@li...
>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>> [2]
> 
> 
> 
> Links:
> ------
> [1]
> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&amp;iu=/4140/ostg.clktrk
> [2] https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
> 
> ------------------------------------------------------------------------------
> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
> from Actuate! Instantly Supercharge Your Business Reports and 
> Dashboards
> with Interactivity, Sharing, Native Excel Exports, App Integration & 
> more
> Get technology previously reserved for billion-dollar corporations, 
> FREE
> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk
> 
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

Re: [DL-Learner discussion] dl-learner and noise

[Lorenz B. <spo...@st...>]

Hello Céline,

there is a noise parameter called "noisePercentage" for the learning 
algorithm CELOE, so you could simply define a noise value like 20%. This 
will allow to return solutions in which 20% of the positive examples are 
not covered by the solution.

Your conf file should be extended by the line:

alg.noisePercentage = "20"

Hope this helps.

Kind regards,
Lorenz

> Hello,
>
> I have an ontology describing 10000 films. I have built a conf file 
> with positive and negative examples for “American films”. I want to 
> learn that an American film “isFromCountry some _US”. When I run 
> DL-Learner, it works well. Now, if I add a little bit of noise 
> (changing one positive example into negative example and vice versa), 
> it does not work anymore. “isFromCountry some Country” is learned 
> (acc=42.86%). I have 10000 films, so this noise represents only 0.02% 
> of error.
> Is it possible to get the good definition with some noise in my 
> examples by running DL-Learner with some parameters I don’t know about 
> or does DL-Learner just not handle noise?
>
> Thanks in advance for your answer. I attached the files if you want to 
> make some tests. I use dllearner-1.0-beta-3 on Windows.
>
> Best regards,
> Céline
>
>
> ------------------------------------------------------------------------------
> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
> from Actuate! Instantly Supercharge Your Business Reports and Dashboards
> with Interactivity, Sharing, Native Excel Exports, App Integration & more
> Get technology previously reserved for billion-dollar corporations, FREE
> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk
>
>
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

[DL-Learner discussion] dl-learner and noise

[alec <Cel...@lr...>]

Hello,

I have an ontology describing 10000 films. I have built a conf file with 
positive and negative examples for “American films”. I want to learn 
that an American film “isFromCountry some _US”. When I run DL-Learner, 
it works well. Now, if I add a little bit of noise (changing one 
positive example into negative example and vice versa), it does not work 
anymore. “isFromCountry some Country” is learned (acc=42.86%). I have 
10000 films, so this noise represents only 0.02% of error.
Is it possible to get the good definition with some noise in my examples 
by running DL-Learner with some parameters I don’t know about or does 
DL-Learner just not handle noise?

Thanks in advance for your answer. I attached the files if you want to 
make some tests. I use dllearner-1.0-beta-3 on Windows.

Best regards,
Céline

Re: [DL-Learner discussion] Some questions and remarks about DL-Learner 1.0-beta-3

[Lorenz B. <spo...@st...>]

Hi Céline,

thanks for your interest and comments. I'll try to answer them.

1. Unfortunately, the manual is quite old and as you pointed out things 
are missing. Basically, the target language depends on the allowed DL 
constructs, which can be set in the refinement operator. In general we 
support ALCQ(D), that means we also support qualified cardinality 
restrictions(Q). The is also one option for hasValue constructs, which 
are some kind of syntactic sugar for existential restrictions combined 
with a singleton nominal cconcept. In that case we would have ALCOQ(D).

2. Currently, only xsd:integer and xsd:double are implemented, but in 
general every numerical xsd datatype is possible as long as there exists 
a corresponding Java class. Let me know if you need support for the 
other datatypes.

3. Well, I thought everything in DL-Learner is based on UTF-8, but it 
sound like an encoding bug. We will investigate it.

4. Yes, you're right. I guess the language is left-bound and based on 
the OR the parser expects a DataUnionOf. We should follow the precedence 
rules and will add parenthesis if necessary. Thanks for the hint.


All the best,
Lorenz
On 27.10.2014 16:10, alec wrote:
> Hello,
>
> I have some questions and remarks on DL-Learner. I would be very 
> grateful if you could answer my questions. I am using 
> dllearner-1.0-beta-3 on Windows.
>
> 1. In the manual, for the learning algorithms, sometimes the target 
> language is written but sometimes it is not. For example, it is 
> ALCN(D) for Refinement but it is not written for CELOE. Would it be 
> possible to add it? Or just to tell me the target language for each 
> algorithm.
>
> 2. Moreover, when “datatype properties” are taken into account, could 
> you tell me exactly with what kind of ranges does it work? For 
> example, I tried to get a solution like
> "hasAge some integer[>=28]"
> with CELOE and it worked. It was working with doubles too. But it was 
> not working with floats. Do you have a list of ranges which are 
> working/ not working?
>
> 3. I also noticed that, when there are accents, the solution can 
> change. For example, the solution is not the same if I just add 
> accents in some words in both the .conf and .owl files.
>
> 4. Finally, I think some parenthesis are missing in solutions with 
> datatype properties. For example, I can get something like
> "hasAge some integer[>= 28] or (not male)".
> I tried to copy it on Protege saying that some new class of mine was 
> equivalent to
> "hasAge some integer[>= 28] or (not male)"
> with the class expression editor. But it said it was expecting 
> something like a datatype name instead of “male”. It is working when I 
> write
> "(hasAge some integer[>=28]) or (not male)".
>
>
> Thank you in advance for your answers. I attached examples in a zip 
> directory if needed.
>
> Best regards,
> Céline
>
>
> ------------------------------------------------------------------------------
>
>
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

[DL-Learner discussion] Some questions and remarks about DL-Learner 1.0-beta-3

[alec <Cel...@lr...>]

Hello,

I have some questions and remarks on DL-Learner. I would be very 
grateful if you could answer my questions. I am using 
dllearner-1.0-beta-3 on Windows.

1. In the manual, for the learning algorithms, sometimes the target 
language is written but sometimes it is not. For example, it is ALCN(D) 
for Refinement but it is not written for CELOE. Would it be possible to 
add it? Or just to tell me the target language for each algorithm.

2. Moreover, when “datatype properties” are taken into account, could 
you tell me exactly with what kind of ranges does it work? For example, 
I tried to get a solution like
"hasAge some integer[>=28]"
with CELOE and it worked. It was working with doubles too. But it was 
not working with floats. Do you have a list of ranges which are working/ 
not working?

3. I also noticed that, when there are accents, the solution can change. 
For example, the solution is not the same if I just add accents in some 
words in both the .conf and .owl files.

4. Finally, I think some parenthesis are missing in solutions with 
datatype properties. For example, I can get something like
"hasAge some integer[>= 28] or (not male)".
I tried to copy it on Protege saying that some new class of mine was 
equivalent to
"hasAge some integer[>= 28] or (not male)"
with the class expression editor. But it said it was expecting something 
like a datatype name instead of “male”. It is working when I write
"(hasAge some integer[>=28]) or (not male)".


Thank you in advance for your answers. I attached examples in a zip 
directory if needed.

Best regards,
Céline

Re: [DL-Learner discussion] Problems to Build

[Jens L. <le...@in...>]

Hello Bruno,

sorry that we missed your mail. If the problem still persists, please 
let us know.

Kind regards,

Jens


Am 12.03.2014 04:04, schrieb Bruno Coimbra:
> Hi,
>
> I am trying to include a dependency of DL-Learner to my maven project in
> NetBeans.
> I follow the instructions shown in the DL-Learner web site to do so.
> However, when I try to build my Project, I get the following error:
>
> cd C:\Users\Bruno\Projetos\dl-learner-ensemble; "JAVA_HOME=C:\\Program
> Files\\Java\\jdk1.7.0_40" cmd /c "\"\"C:\\Program Files\\NetBeans
> 7.4\\java\\maven\\bin\\mvn.bat\" -Dexec.args=\"-classpath %classpath
> edu.uff.dllearnerensemble.App\" -Dexec.executable=\"C:\\Program
> Files\\Java\\jdk1.7.0_40\\bin\\java.exe\" -DnetbeansProjectMappings=
> -Dmaven.ext.class.path=\"C:\\Program Files\\NetBeans
> 7.4\\java\\maven-nblib\\netbeans-eventspy.jar\"
> org.codehaus.mojo:exec-maven-plugin:1.2.1:exec\""
> Running NetBeans Compile On Save execution. Phase execution is skipped
> and output directories of dependency projects (with Compile on Save
> turned on) will be used instead of their jar artifacts.
> Scanning for projects...
>
> ------------------------------------------------------------------------
> Building dl-learner-ensemble 1.0-SNAPSHOT
> ------------------------------------------------------------------------
> Downloading:
> http://prod1.aksw.org:8081/archiva/repository/snapshots/org/dllearner/components-core/1.0-SNAPSHOT/maven-metadata.xml
>
> Could not transfer metadata
> org.dllearner:components-core:1.0-SNAPSHOT/maven-metadata.xml from/to
> snapshot.maven.aksw
> (http://prod1.aksw.org:8081/archiva/repository/snapshots/): Connection
> to http://prod1.aksw.org:8081 refused
> Failure to transfer
> org.dllearner:components-core:1.0-SNAPSHOT/maven-metadata.xml from
> http://prod1.aksw.org:8081/archiva/repository/snapshots/ was cached in
> the local repository, resolution will not be reattempted until the
> update interval of snapshot.maven.aksw has elapsed or updates are
> forced. Original error: Could not transfer metadata
> org.dllearner:components-core:1.0-SNAPSHOT/maven-metadata.xml from/to
> snapshot.maven.aksw
> (http://prod1.aksw.org:8081/archiva/repository/snapshots/): Connection
> to http://prod1.aksw.org:8081 refused
> Downloading:
> http://prod1.aksw.org:8081/archiva/repository/snapshots/org/dllearner/components-core/1.0-SNAPSHOT/components-core-1.0-SNAPSHOT.pom
>
> ------------------------------------------------------------------------
> BUILD FAILURE
> ------------------------------------------------------------------------
> Total time: 42.609s
> Finished at: Tue Mar 11 23:58:11 BRT 2014
> Final Memory: 5M/123M
> ------------------------------------------------------------------------
> Failed to execute goal on project dl-learner-ensemble: Could not resolve
> dependencies for project edu.uff:dl-learner-ensemble:jar:1.0-SNAPSHOT:
> Failed to collect dependencies for [junit:junit:jar:3.8.1 (test),
> org.dllearner:components-core:jar:1.0-SNAPSHOT (compile)]: Failed to
> read artifact descriptor for
> org.dllearner:components-core:jar:1.0-SNAPSHOT: Could not transfer
> artifact org.dllearner:components-core:pom:1.0-SNAPSHOT from/to
> snapshot.maven.aksw
> (http://prod1.aksw.org:8081/archiva/repository/snapshots/): Connection
> to http://prod1.aksw.org:8081 refused: Connection timed out: connect ->
> [Help 1]
>
> To see the full stack trace of the errors, re-run Maven with the -e switch.
> Re-run Maven using the -X switch to enable full debug logging.
>
> For more information about the errors and possible solutions, please
> read the following articles:
> [Help 1]
> http://cwiki.apache.org/confluence/display/MAVEN/DependencyResolutionException
>
> Apparently I am having my connection refused.
> Am I doing something wrong?
>
> Thank you,
> Bruno Coimbra
>
> Enviado do Email do Windows
>
>
>
> ------------------------------------------------------------------------------
>
>
>
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>


-- 
Dr. Jens Lehmann
Head of AKSW group, University of Leipzig
Homepage: http://www.jens-lehmann.org
Group: http://aksw.org - semantic web research center
Project: http://geoknow.eu - geospatial data on the web

Re: [DL-Learner discussion] Questions on DL-Learner

[alec <Cel...@lr...>]

Hi Lorenz,

It is working now and I get the kind of rules I wanted.
Thanks!

Regards,
Céline


Le 26/05/2014 17:08, Lorenz Bühmann a écrit :
> Hi Céline,
>
>  yes, it seems that Windows has same weird restrictions on the length
> of a command line call as you can read here:
>
>  " Windows has a limit (1KB - 2KB) of characters you can have on one
> single command line. "
>
>  Unfortunately the automatically build batch script violates this
> restrictions because the Java classpath is quite long. As a 
> workaround
> can you can try the attached batch file. Additionally, you'll need
> Java 7 for the latest DL-Learner.
>
>  Hope this helps.
>
>  Regards,
>  Lorenz
>
> On 05/23/2014 03:56 PM, alec wrote:
>
>> The input line is too long

Re: [DL-Learner discussion] Questions on DL-Learner

[alec <Cel...@lr...>]

Hi Lorenz,

I cannot make it work... I am on Windows:

--------------------------------------------------------------------
...\dllearner-1.0-beta-3\bin>cli.bat ../examples/father.conf
La ligne entrée est trop longue.
La syntaxe de la commande n'est pas correcte.
--------------------------------------------------------------------
which means
The input line is too long.
The syntax of the command is not correct.

This same command line works on dllearner-1.0-beta-2.

Regards,
Céline



Le 23/05/2014 14:05, Lorenz Bühmann a écrit :
> Hi Céline,
>
>  can you check if the latest version dllearner-1.0-beta-3 [15] which
> is downloadable at
> https://sourceforge.net/projects/dl-learner/files/DL-Learner/ [16]
> works for you?
>
>  Regards,
>  Lorenz
>
> On 04/29/2014 09:48 AM, alec wrote:
>
>> Hi Lorenz,
>>
>> Thanks for your answer, you were right, it was not the latest
>> version.
>> I tried the latest version on my "father_test" example. I got that:
>>
>>
>> ------------------------------------------------------
>> DL-Learner command line interface
>> Initializing Component "OWL File"... OK (0ms)
>> Initializing Component "fast instance checker"... OK (490ms)
>> Initializing Component "PosNegLPStandard"... OK (0ms)
>> Initializing Component "CELOE"... OK (20ms)
>> Initializing Component "PCELOE"... OK (0ms)
>> Running algorithm instance "alg1"(CELOE)
>> more accurate (50,00%) class expression found: Thing
>> more accurate (83,33%) class expression found: male
>> Algorithm terminated successfully (time: 10s 0ms, 373537
>> descriptions tested, 22
>> 1907 nodes in the search tree).
>>
>> number of retrievals: 6
>> retrieval reasoning time: 0ms ( 0ms per retrieval)
>> number of instance checks: 4145956 (0 multiple)
>> instance check reasoning time: 452ms ( 0ms per instance check)
>> (complex) subsumption checks: 266 (0 multiple)
>> subsumption reasoning time: 96ms ( 0ms per subsumption check)
>> overall reasoning time: 549ms
>>
>> solutions:
>> 1: male (pred. acc.: 83,33%, F-measure: 85,71%)
>> 2: (not female) (pred. acc.: 83,33%, F-measure: 85,71%)
>> 3: Thing (pred. acc.: 50,00%, F-measure: 66,67%)
>> 4: (female or male) (pred. acc.: 50,00%, F-measure: 66,67%)
>> 5: (male or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
>> 6: (female or (not female)) (pred. acc.: 50,00%, F-measure: 66,67%)
>>
>> 7: ((not female) or (not male)) (pred. acc.: 50,00%, F-measure:
>> 66,67%)
>>
>> Running algorithm instance "alg2"(PCELOE)
>> more accurate (50,00%) class expression found: Thing
>> more accurate (83,33%) class expression found: male
>> Algorithm terminated successfully (time: 10s 52ms, 516119
>> descriptions tested, 3
>> 28678 nodes in the search tree).
>>
>> number of retrievals: 12
>> retrieval reasoning time: 0ms ( 0ms per retrieval)
>> number of instance checks: 10208696 (0 multiple)
>> instance check reasoning time: 1s 111ms ( 0ms per instance check)
>> (complex) subsumption checks: 552 (0 multiple)
>> subsumption reasoning time: 163ms ( 0ms per subsumption check)
>> overall reasoning time: 1s 274ms
>>
>> solutions:
>> 1: male (pred. acc.: 83,33%, F-measure: 85,71%)
>> 2: (not female) (pred. acc.: 83,33%, F-measure: 85,71%)
>> 3: Thing (pred. acc.: 50,00%, F-measure: 66,67%)
>> 4: (female or male) (pred. acc.: 50,00%, F-measure: 66,67%)
>> 5: (male or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
>> 6: (female or (not female)) (pred. acc.: 50,00%, F-measure: 66,67%)
>>
>> 7: ((not female) or (not male)) (pred. acc.: 50,00%, F-measure:
>> 66,67%)
>> -------------------------------------------------------------
>>
>> So, there is no solution with something like "hasAge
>> integer[>=28]". Maybe, I did not use the correct parameters. I used
>> the same file than "father.conf" (I just changed ks.fileName).
>>
>> Regards,
>> Céline.
>>
>> Le 12.04.2014 11:51, Lorenz Bühmann a écrit :
>>
>>> Hi,
>>>
>>> which version if DL-Learner do you use? The latest version online
>>> is
>>>
>>>
>>
> 
> http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-2.tar.gz/download
>>> [4]
>>> [7] but we plan to upload a new one as there are many new
>>> features and
>>> bugfixes in the current SVN version.
>>> Form the error message, I assume you use a quite old version. Can
>>> you
>>> try the latest version?
>>>
>>> According to the modified example:
>>>
>>> You have as positive examples:
>>> stefan -> male, 28
>>> markus -> male, 50
>>> martin -> male, 34
>>> and negative examples:
>>> heinz -> male, 17
>>> anna -> female, 10
>>> michelle -> female, 4
>>>
>>> So I guess you want to learn something like
>>> "male AND hasAge integer[>=34]" ?
>>>
>>> I'll check if this works, but it should be possible.
>>>
>>> Regards,
>>> Lorenz
>>>
>>> On 04/10/2014 10:53 AM, alec wrote:
>>>
>>>> Hi Lorenz,
>>>>
>>>> Thank you very much for your answers.
>>>> I'm planning to use DL-Learner to learn concept definitions
>>>> from an
>>>> ontology of holiday destinations (I don't have the ontology
>>>> yet). I
>>>> want to make sure it is possible to get definitions with
>>>> inferiority/superiority signs (about numerical datatype
>>>> properties
>>>> not about cardinality restrictions).
>>>> For example, I would like to get something like that:
>>>> "Definition of a destination which is hot in Winter:
>>>> hasJanuaryTemperature x and
>>>> hasFebruaryTemperature y and
>>>> hasMarchTemperature z and
>>>> x>20 and
>>>> y>20 and
>>>> z>20".
>>>>
>>>> I tried to modify the "father.owl" file (see attachments) in
>>>> DL-Learner examples. I put a "hasAge" datatype property and I
>>>> deleted "hasChild". I was hoping to see if I could get a
>>>> definition
>>>> with a superiority/inferiority sign about age. I got that:
>>>>
>>>> DL-Learner 2010-08-07 command line interface
>>>> starting component manager ... OK (82ms)
>>>> initialising component "OWL file" ... OK (0ms)
>>>> initialising component "fast instance checker" ... OK (388ms)
>>>> initialising component "pos neg learning problem" ... OK (0ms)
>>>> initialising component "OCEL" ... OK (14ms)
>>>>
>>>> starting top down refinement with: Thing (50% accuracy)
>>>> more accurate (83,33%) class expression found: male
>>>> Exception in thread "main" java.lang.OutOfMemoryError: GC
>>>> overhead
>>>> limit exceeded
>>>> at java.util.LinkedList.linkLast(Unknown Source)
>>>> at java.util.LinkedList.add(Unknown Source)
>>>> at java.util.LinkedList.clone(Unknown Source)
>>>> at
>>>
>>>
>>
> 
> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:474)
>>>
>>>
>>>> at
>>>
>>>
>>
> 
> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:498)
>>>
>>>
>>> at
>>>
>>>
>>
> 
> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:470)
>>>
>>>
>>> at
>>>
>>>
>>
> 
> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:413)
>>>
>>>
>>> at
>>>
>>>
>>
> org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLe
>>>
>>>> at
>>> /blockquote>
>>>
>>
> 
> org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLearner2.java:521)
>>>
>>> at
>>>
>>>> org.dllearner.algorithms.refinement2.ROLearner2.start(ROLe
>>> 36)
>>> at
>>>
>>>
>>
> 
> org.dllearner.algorithms.refinement2.ROLComponent2.start(ROLComponent2.java
>>>
>>>> at org.dllearner.cli.Start.sta
>>> :347)
>>> at org.dllearner.cli.Start.main(Start.java:209)
>>>
>>> Kind regards,
>>>
>>>> Le 1
>>> 02, Lorenz Bühmann a écrit :
>>>
>>> Hi Céline,
>>>
>>> of course
>>>
>>>> ormation about DL-Learner if
>>> br> you're
>>> interested in.
>>>
>>> 1.) I'm not exactly sure what you mean by target langua
>>>
>>>> if you
>>> refer to what's the expressivity of the learned class
>>> expressions, then
>>> no, the ta
>>>
>>>> ner is not ALC.
>>>> Depending on the used learning algorithm, DL-Learner of course
>>>> supports
>>>> datatype properties and for example can also learn class
>>>> expressions
>>>> which consist of constructs used in Description Logics beyond
>>>> ALC, like
>>>> for example qualified cardinality restrictions(Q).
>>>>
>>>> 2.) see 1.)
>>>>
>>>> 3.) We do not have any numbers, but in general the internally
>>>> used OWL
>>>> reasoner(e.g. Pellet or HermiT) might be a bottleneck. If
>>>> you're
>>>> able to
>>>> just load the necessary part of the ontology, this can of
>>>> course
>>>> positively influence the learning process. Maybe we're both
>>>> taking about
>>>> different things when using the term "noise", but I wouldn't
>>>> declare
>>>> unnecessary information as noise.
>>>>
>>>> 4.) Limits in which sense?
>>>>
>>>> Can you give us any insights into what you're planning to do
>>>> with
>>>> the
>>>> DL-Learner?
>>>>
>>>> Kind regards,
>>>> Lorenz
>>>> On 04/08/2014 09:00 AM, alec wrote:
>>>>
>>>>> Hello,
>>>>>
>>>>> I am a PhD student in Laboratoire de Recherche en
>>>>> Informatique
>>>>> in
>>>>> Université Paris Sud (France). I have read papers on
>>>>> DL-Learner. For my
>>>>> thesis project, I might be interested in using an ILP tool to
>>>>>
>>>>> learn
>>>>> concept definitions. But the ontology I will use as input
>>>>> will
>>>>> have
>>>>> datatype properties (numerical values) and I would like to
>>>>> use
>>>>> a tool
>>>>> which can learn
>>>>> definitions using these datatype properties.
>>>>>
>>>>> I would like to have some additional information on
>>>>> DL-Learner
>>>>> if it is
>>>>> possible. I would be grateful if you could answer my
>>>>> questions.
>>>>>
>>>>> 1. I understood that the target language of your algorithm is
>>>>>
>>>>> ALC
>>>>> description logic. Can you confirm me that we cannot get a
>>>>> definition of
>>>>> a concept with datatype properties (other than string
>>>>> datatype
>>>>> properties)?
>>>>> For example, something like an adult is a person whose age
>>>>> hasValue x
>>>>> with x>=18.
>>>>>
>>>>> 2. If I understood right:
>>>>> Is there a particular reason for that? Has it a real
>>>>> complexity
>>>>> to
>>>>> implement? Or do you know tools (open source or free of
>>>>> charge
>>>>> for
>>>>> academic research) that can generate a definition with
>>>>> numerical
>>>>> datatype properties (e.g. in SHOIN(D) description logic)?
>>>>>
>>>>> 3. Are there any constraints about the input ontology? Can it
>>>>>
>>>>> be a big
>>>>> ontology with potential information which is not interesting
>>>>> for
>>>>> defining a concept (i.e. with noise)? Or has it to be just
>>>>> the
>>>>> interesting part of the ontology?
>>>>>
>>>>> 4. Can you say what the limits of DL-Learner are?
>>>>>
>>>>> I would greatly appreciate any help you might be able to give
>>>>>
>>>>> me.
>>>>>
>>>>> Best regards,
>>>>> Céline Alec
>>>>
>>>>
>>>
>>
> 
> ------------------------------------------------------------------------------
>>>>
>>>>
>>>> Put Bad Developers to Shame
>>>> Dominate Development with Jenkins Continuous Integration
>>>> Continuously Automate Build, Test & Deployment
>>>> Start a new project now. Try Jenkins in the cloud.
>>>> http://p.sf.net/sfu/13600_Cloudbees [1] [1]
>>>> _______________________________________________
>>>> dl-learner-discussion mailing list
>>> dl-...@li... [2]
>>>
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>> [5]
>>> [6]
>>>
>>>
>>
> 
> ------------------------------------------------------------------------------
>>>
>>> Put Bad Developers to Shame
>>> Dominate Development with Jenkins Continuous Integration
>>> Continuously Automate Build, Test & Deployment
>>> Start a new project now. Try Jenkins in the cloud.
>>> http://p.sf.net/sfu/13600_Cloudbees [6]
>>>
>>> _______________________________________________
>>> dl-learner-discussion mailing list
>>> dl-...@li... [7]
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>> [8]
>>>
>>> Links:
>>> ------
>>> [1] http://p.sf.net/sfu/13600_Cloudbees [9]
>>> [2] mailto:dl-...@li... [10]
>>> [3]
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>> [11]
>>> [4] http://p.sf.net/sfu/13600_Cloudbees [12]
>>> [5] mailto:dl-...@li... [13]
>>> [6]
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>> [14]
>>> [7]
>>
>>
> 
> ------------------------------------------------------------------------------
>> "Accelerate Dev Cycles with Automated Cross-Browser Testing - For
>> FREE
>> Instantly run your Selenium tests across 300+ browser/OS combos. Get
>>
>> unparalleled scalability from the best Selenium testing platform
>> available.
>> Simple to use. Nothing to install. Get started now for free."
>> http://p.sf.net/sfu/SauceLabs
>>
>> _______________________________________________
>> dl-learner-discussion mailing list
>> dl-...@li...
>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>
>
>
> Links:
> ------
> [1] http://p.sf.net/sfu/13600_Cloudbees
> [2] http://p.sf.net/sfu/13600_Cloudbees
> [3] mailto:dl-...@li...
> [4]
> 
> http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-2.tar.gz/download
> [5] https://lists.s<div>
>
> 
> &gt;&nbsp;tinfo/dl-learner-discussion&quot;&gt;https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
> &gt;&nbsp;
> &gt;&nbsp;
> &gt;&gt;&nbsp;&nbsp;[3]
> &gt;&nbsp;
> 
> &gt;&nbsp;&nbsp;------------------------------------------------------------------------------
> &gt;&nbsp;
> &gt;&nbsp;
> &gt;&nbsp;&nbsp;Put&nbsp;Bad&nbsp;Developers&nbsp;to&nbsp;Shame&nbsp;
> 
> &gt;&nbsp;&nbsp;Dominate&nbsp;Development&nbsp;with&nbsp;Jenkins&nbsp;Continuous&nbsp;Integration&nbsp;
> 
> &gt;&nbsp;&nbsp;Continuously&nbsp;Automate&nbsp;Build,&nbsp;Test&nbsp;&amp;&nbsp;Deployment&nbsp;
> 
> &gt;&nbsp;&nbsp;Start&nbsp;a&nbsp;new&nbsp;project&nbsp;now.&nbsp;Try&nbsp;Jenkins&nbsp;in&nbsp;the&nbsp;cloud.&nbsp;
> 
> &gt;&nbsp;&nbsp;http://p.sf.net/sfu/13600_Cloudbees&nbsp;[2]&nbsp;[4]&nbsp;
> &gt;&nbsp;&nbsp;_______________________________________________&nbsp;
> &gt;&nbsp;&nbsp;dl-learner-discussion&nbsp;mailing&nbsp;list&nbsp;
> 
> &gt;&nbsp;&nbsp;dl-...@li...&nbsp;[3]&nbsp;[5]
> </div>rceforge.net/lists/listinfo/dl-learner-discussion
> [6] http://p.sf.net/sfu/13600_Cloudbees
> [7] mailto:dl-...@li...
> [8] 
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
> [9] http://p.sf.net/sfu/13600_Cloudbees
> [10] mailto:dl-...@li...
> [11] 
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
> [12] http://p.sf.net/sfu/13600_Cloudbees
> [13] mailto:dl-...@li...
> [14] 
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
> [15]
> 
> http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-3.zip/download
> [16] https://sourceforge.net/projects/dl-learner/files/DL-Learner/

Re: [DL-Learner discussion] Questions on DL-Learner

[Lorenz B. <spo...@st...>]

Hi Céline,

can you check if the latest version dllearner-1.0-beta-3 
<http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-3.zip/download> 
which is downloadable at 
https://sourceforge.net/projects/dl-learner/files/DL-Learner/ works for you?

Regards,
Lorenz
On 04/29/2014 09:48 AM, alec wrote:
> Hi Lorenz,
>
> Thanks for your answer, you were right, it was not the latest version.
> I tried the latest version on my "father_test" example. I got that:
>
> ------------------------------------------------------
> DL-Learner command line interface
> Initializing Component "OWL File"... OK (0ms)
> Initializing Component "fast instance checker"... OK (490ms)
> Initializing Component "PosNegLPStandard"... OK (0ms)
> Initializing Component "CELOE"... OK (20ms)
> Initializing Component "PCELOE"... OK (0ms)
> Running algorithm instance "alg1"(CELOE)
> more accurate (50,00%) class expression found: Thing
> more accurate (83,33%) class expression found: male
> Algorithm terminated successfully (time: 10s 0ms, 373537 descriptions 
> tested, 22
> 1907 nodes in the search tree).
>
> number of retrievals: 6
> retrieval reasoning time: 0ms ( 0ms per retrieval)
> number of instance checks: 4145956 (0 multiple)
> instance check reasoning time: 452ms ( 0ms per instance check)
> (complex) subsumption checks: 266 (0 multiple)
> subsumption reasoning time: 96ms ( 0ms per subsumption check)
> overall reasoning time: 549ms
>
> solutions:
> 1: male (pred. acc.: 83,33%, F-measure: 85,71%)
> 2: (not female) (pred. acc.: 83,33%, F-measure: 85,71%)
> 3: Thing (pred. acc.: 50,00%, F-measure: 66,67%)
> 4: (female or male) (pred. acc.: 50,00%, F-measure: 66,67%)
> 5: (male or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
> 6: (female or (not female)) (pred. acc.: 50,00%, F-measure: 66,67%)
> 7: ((not female) or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
>
> Running algorithm instance "alg2"(PCELOE)
> more accurate (50,00%) class expression found: Thing
> more accurate (83,33%) class expression found: male
> Algorithm terminated successfully (time: 10s 52ms, 516119 descriptions 
> tested, 3
> 28678 nodes in the search tree).
>
> number of retrievals: 12
> retrieval reasoning time: 0ms ( 0ms per retrieval)
> number of instance checks: 10208696 (0 multiple)
> instance check reasoning time: 1s 111ms ( 0ms per instance check)
> (complex) subsumption checks: 552 (0 multiple)
> subsumption reasoning time: 163ms ( 0ms per subsumption check)
> overall reasoning time: 1s 274ms
>
> solutions:
> 1: male (pred. acc.: 83,33%, F-measure: 85,71%)
> 2: (not female) (pred. acc.: 83,33%, F-measure: 85,71%)
> 3: Thing (pred. acc.: 50,00%, F-measure: 66,67%)
> 4: (female or male) (pred. acc.: 50,00%, F-measure: 66,67%)
> 5: (male or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
> 6: (female or (not female)) (pred. acc.: 50,00%, F-measure: 66,67%)
> 7: ((not female) or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
> -------------------------------------------------------------
>
> So, there is no solution with something like "hasAge integer[>=28]". 
> Maybe, I did not use the correct parameters. I used the same file than 
> "father.conf" (I just changed ks.fileName).
>
> Regards,
> Céline.
>
>
>
> Le 12.04.2014 11:51, Lorenz Bühmann a écrit :
>> Hi,
>>
>>  which version if DL-Learner do you use? The latest version online is
>>
>> http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-2.tar.gz/download 
>>
>> [7] but we plan to upload a new one as there are many new features and
>> bugfixes in the current SVN version.
>>  Form the error message, I assume you use a quite old version. Can you
>> try the latest version?
>>
>>  According to the modified example:
>>
>>  You have as positive examples:
>>  stefan -> male, 28
>>  markus -> male, 50
>>  martin -> male, 34
>>  and negative examples:
>>  heinz -> male, 17
>>  anna -> female, 10
>>  michelle -> female, 4
>>
>>  So I guess you want to learn something like
>>  "male AND hasAge integer[>=34]" ?
>>
>>  I'll check if this works, but it should be possible.
>>
>>  Regards,
>>  Lorenz
>>
>> On 04/10/2014 10:53 AM, alec wrote:
>>
>>> Hi Lorenz,
>>>
>>> Thank you very much for your answers.
>>> I'm planning to use DL-Learner to learn concept definitions from an
>>> ontology of holiday destinations (I don't have the ontology yet). I
>>> want to make sure it is possible to get definitions with
>>> inferiority/superiority signs (about numerical datatype properties
>>> not about cardinality restrictions).
>>> For example, I would like to get something like that:
>>> "Definition of a destination which is hot in Winter:
>>> hasJanuaryTemperature x and
>>> hasFebruaryTemperature y and
>>> hasMarchTemperature z and
>>> x>20 and
>>> y>20 and
>>> z>20".
>>>
>>> I tried to modify the "father.owl" file (see attachments) in
>>> DL-Learner examples. I put a "hasAge" datatype property and I
>>> deleted "hasChild". I was hoping to see if I could get a definition
>>> with a superiority/inferiority sign about age. I got that:
>>>
>>> DL-Learner 2010-08-07 command line interface
>>> starting component manager ... OK (82ms)
>>> initialising component "OWL file" ... OK (0ms)
>>> initialising component "fast instance checker" ... OK (388ms)
>>> initialising component "pos neg learning problem" ... OK (0ms)
>>> initialising component "OCEL" ... OK (14ms)
>>>
>>> starting top down refinement with: Thing (50% accuracy)
>>> more accurate (83,33%) class expression found: male
>>> Exception in thread "main" java.lang.OutOfMemoryError: GC overhead
>>> limit exceeded
>>> at java.util.LinkedList.linkLast(Unknown Source)
>>> at java.util.LinkedList.add(Unknown Source)
>>> at java.util.LinkedList.clone(Unknown Source)
>>> at
>>>
>>
>> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:474)
>>>
>>> at
>>>
>>
>> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:498)
>>>
>>> at
>>>
>>
>> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:470)
>>>
>>> at
>>>
>>
>> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:413)
>>>
>>> at
>>>
>>
>> org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLearner2.java:551) 
>>
>>> at
>>>
>>
>> org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLearner2.java:521) 
>>
>>> at
>>>
>>
>> org.dllearner.algorithms.refinement2.ROLearner2.start(ROLearner2.java:436) 
>>
>>> at
>>>
>>
>> org.dllearner.algorithms.refinement2.ROLComponent2.start(ROLComponent2.java:441) 
>>
>>> at org.dllearner.cli.Start.start(Start.java:347)
>>> at org.dllearner.cli.Start.main(Start.java:209)
>>>
>>> Kind regards,
>>> Céline
>>>
>>> Le 10.04.2014 00:02, Lorenz Bühmann a écrit :
>>>
>>>> Hi Céline,
>>>>
>>>> of course we can give you more information about DL-Learner if
>>>> you're
>>>> interested in.
>>>>
>>>> 1.) I'm not exactly sure what you mean by target language, but if
>>>> if you
>>>> refer to what's the expressivity of the learned class
>>>> expressions, then
>>>> no, the target language of DL-Learner is not ALC.
>>>> Depending on the used learning algorithm, DL-Learner of course
>>>> supports
>>>> datatype properties and for example can also learn class
>>>> expressions
>>>> which consist of constructs used in Description Logics beyond
>>>> ALC, like
>>>> for example qualified cardinality restrictions(Q).
>>>>
>>>> 2.) see 1.)
>>>>
>>>> 3.) We do not have any numbers, but in general the internally
>>>> used OWL
>>>> reasoner(e.g. Pellet or HermiT) might be a bottleneck. If you're
>>>> able to
>>>> just load the necessary part of the ontology, this can of course
>>>> positively influence the learning process. Maybe we're both
>>>> taking about
>>>> different things when using the term "noise", but I wouldn't
>>>> declare
>>>> unnecessary information as noise.
>>>>
>>>> 4.) Limits in which sense?
>>>>
>>>> Can you give us any insights into what you're planning to do with
>>>> the
>>>> DL-Learner?
>>>>
>>>> Kind regards,
>>>> Lorenz
>>>> On 04/08/2014 09:00 AM, alec wrote:
>>>>
>>>>> Hello,
>>>>>
>>>>> I am a PhD student in Laboratoire de Recherche en Informatique
>>>>> in
>>>>> Université Paris Sud (France). I have read papers on
>>>>> DL-Learner. For my
>>>>> thesis project, I might be interested in using an ILP tool to
>>>>> learn
>>>>> concept definitions. But the ontology I will use as input will
>>>>> have
>>>>> datatype properties (numerical values) and I would like to use
>>>>> a tool
>>>>> which can learn
>>>>> definitions using these datatype properties.
>>>>>
>>>>> I would like to have some additional information on DL-Learner
>>>>> if it is
>>>>> possible. I would be grateful if you could answer my questions.
>>>>>
>>>>>
>>>>> 1. I understood that the target language of your algorithm is
>>>>> ALC
>>>>> description logic. Can you confirm me that we cannot get a
>>>>> definition of
>>>>> a concept with datatype properties (other than string datatype
>>>>> properties)?
>>>>> For example, something like an adult is a person whose age
>>>>> hasValue x
>>>>> with x>=18.
>>>>>
>>>>> 2. If I understood right:
>>>>> Is there a particular reason for that? Has it a real complexity
>>>>> to
>>>>> implement? Or do you know tools (open source or free of charge
>>>>> for
>>>>> academic research) that can generate a definition with
>>>>> numerical
>>>>> datatype properties (e.g. in SHOIN(D) description logic)?
>>>>>
>>>>> 3. Are there any constraints about the input ontology? Can it
>>>>> be a big
>>>>> ontology with potential information which is not interesting
>>>>> for
>>>>> defining a concept (i.e. with noise)? Or has it to be just the
>>>>> interesting part of the ontology?
>>>>>
>>>>> 4. Can you say what the limits of DL-Learner are?
>>>>>
>>>>> I would greatly appreciate any help you might be able to give
>>>>> me.
>>>>>
>>>>> Best regards,
>>>>> Céline Alec
>>>>>
>>>>>
>>>>
>>>
>>
>> ------------------------------------------------------------------------------ 
>>
>>>>>
>>>>> Put Bad Developers to Shame
>>>>> Dominate Development with Jenkins Continuous Integration
>>>>> Continuously Automate Build, Test & Deployment
>>>>> Start a new project now. Try Jenkins in the cloud.
>>>>> http://p.sf.net/sfu/13600_Cloudbees [1]
>>>>> _______________________________________________
>>>>> dl-learner-discussion mailing list
>>>>> dl-...@li... [2]
>>>>>
>>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>>>> [3]
>>>>
>>>>
>>>
>>
>> ------------------------------------------------------------------------------ 
>>
>>>>
>>>> Put Bad Developers to Shame
>>>> Dominate Development with Jenkins Continuous Integration
>>>> Continuously Automate Build, Test & Deployment
>>>> Start a new project now. Try Jenkins in the cloud.
>>>> http://p.sf.net/sfu/13600_Cloudbees [4]
>>>> _______________________________________________
>>>> dl-learner-discussion mailing list
>>>> dl-...@li... [5]
>>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>>> [6]
>>>
>>>
>>
>> ------------------------------------------------------------------------------ 
>>
>>> Put Bad Developers to Shame
>>> Dominate Development with Jenkins Continuous Integration
>>> Continuously Automate Build, Test & Deployment
>>> Start a new project now. Try Jenkins in the cloud.
>>> http://p.sf.net/sfu/13600_Cloudbees
>>>
>>> _______________________________________________
>>> dl-learner-discussion mailing list
>>> dl-...@li...
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>
>>
>>
>> Links:
>> ------
>> [1] http://p.sf.net/sfu/13600_Cloudbees
>> [2] mailto:dl-...@li...
>> [3] https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>> [4] http://p.sf.net/sfu/13600_Cloudbees
>> [5] mailto:dl-...@li...
>> [6] https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>> [7]
>>
>> http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-2.tar.gz/download 
>>
>
>
> ------------------------------------------------------------------------------
> "Accelerate Dev Cycles with Automated Cross-Browser Testing - For FREE
> Instantly run your Selenium tests across 300+ browser/OS combos.  Get
> unparalleled scalability from the best Selenium testing platform available.
> Simple to use. Nothing to install. Get started now for free."
> http://p.sf.net/sfu/SauceLabs
>
>
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

Re: [DL-Learner discussion] Questions on DL-Learner

[alec <Cel...@lr...>]

Hi Lorenz,

Thanks for your answer, you were right, it was not the latest version.
I tried the latest version on my "father_test" example. I got that:

------------------------------------------------------
DL-Learner command line interface
Initializing Component "OWL File"... OK (0ms)
Initializing Component "fast instance checker"... OK (490ms)
Initializing Component "PosNegLPStandard"... OK (0ms)
Initializing Component "CELOE"... OK (20ms)
Initializing Component "PCELOE"... OK (0ms)
Running algorithm instance "alg1"(CELOE)
more accurate (50,00%) class expression found: Thing
more accurate (83,33%) class expression found: male
Algorithm terminated successfully (time: 10s 0ms, 373537 descriptions 
tested, 22
1907 nodes in the search tree).

number of retrievals: 6
retrieval reasoning time: 0ms ( 0ms per retrieval)
number of instance checks: 4145956 (0 multiple)
instance check reasoning time: 452ms ( 0ms per instance check)
(complex) subsumption checks: 266 (0 multiple)
subsumption reasoning time: 96ms ( 0ms per subsumption check)
overall reasoning time: 549ms

solutions:
1: male (pred. acc.: 83,33%, F-measure: 85,71%)
2: (not female) (pred. acc.: 83,33%, F-measure: 85,71%)
3: Thing (pred. acc.: 50,00%, F-measure: 66,67%)
4: (female or male) (pred. acc.: 50,00%, F-measure: 66,67%)
5: (male or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
6: (female or (not female)) (pred. acc.: 50,00%, F-measure: 66,67%)
7: ((not female) or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)

Running algorithm instance "alg2"(PCELOE)
more accurate (50,00%) class expression found: Thing
more accurate (83,33%) class expression found: male
Algorithm terminated successfully (time: 10s 52ms, 516119 descriptions 
tested, 3
28678 nodes in the search tree).

number of retrievals: 12
retrieval reasoning time: 0ms ( 0ms per retrieval)
number of instance checks: 10208696 (0 multiple)
instance check reasoning time: 1s 111ms ( 0ms per instance check)
(complex) subsumption checks: 552 (0 multiple)
subsumption reasoning time: 163ms ( 0ms per subsumption check)
overall reasoning time: 1s 274ms

solutions:
1: male (pred. acc.: 83,33%, F-measure: 85,71%)
2: (not female) (pred. acc.: 83,33%, F-measure: 85,71%)
3: Thing (pred. acc.: 50,00%, F-measure: 66,67%)
4: (female or male) (pred. acc.: 50,00%, F-measure: 66,67%)
5: (male or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
6: (female or (not female)) (pred. acc.: 50,00%, F-measure: 66,67%)
7: ((not female) or (not male)) (pred. acc.: 50,00%, F-measure: 66,67%)
-------------------------------------------------------------

So, there is no solution with something like "hasAge integer[>=28]". 
Maybe, I did not use the correct parameters. I used the same file than 
"father.conf" (I just changed ks.fileName).

Regards,
Céline.



Le 12.04.2014 11:51, Lorenz Bühmann a écrit :
> Hi,
>
>  which version if DL-Learner do you use? The latest version online is
> 
> http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-2.tar.gz/download
> [7] but we plan to upload a new one as there are many new features 
> and
> bugfixes in the current SVN version.
>  Form the error message, I assume you use a quite old version. Can 
> you
> try the latest version?
>
>  According to the modified example:
>
>  You have as positive examples:
>  stefan -> male, 28
>  markus -> male, 50
>  martin -> male, 34
>  and negative examples:
>  heinz -> male, 17
>  anna -> female, 10
>  michelle -> female, 4
>
>  So I guess you want to learn something like
>  "male AND hasAge integer[>=34]" ?
>
>  I'll check if this works, but it should be possible.
>
>  Regards,
>  Lorenz
>
> On 04/10/2014 10:53 AM, alec wrote:
>
>> Hi Lorenz,
>>
>> Thank you very much for your answers.
>> I'm planning to use DL-Learner to learn concept definitions from an
>> ontology of holiday destinations (I don't have the ontology yet). I
>> want to make sure it is possible to get definitions with
>> inferiority/superiority signs (about numerical datatype properties
>> not about cardinality restrictions).
>> For example, I would like to get something like that:
>> "Definition of a destination which is hot in Winter:
>> hasJanuaryTemperature x and
>> hasFebruaryTemperature y and
>> hasMarchTemperature z and
>> x>20 and
>> y>20 and
>> z>20".
>>
>> I tried to modify the "father.owl" file (see attachments) in
>> DL-Learner examples. I put a "hasAge" datatype property and I
>> deleted "hasChild". I was hoping to see if I could get a definition
>> with a superiority/inferiority sign about age. I got that:
>>
>> DL-Learner 2010-08-07 command line interface
>> starting component manager ... OK (82ms)
>> initialising component "OWL file" ... OK (0ms)
>> initialising component "fast instance checker" ... OK (388ms)
>> initialising component "pos neg learning problem" ... OK (0ms)
>> initialising component "OCEL" ... OK (14ms)
>>
>> starting top down refinement with: Thing (50% accuracy)
>> more accurate (83,33%) class expression found: male
>> Exception in thread "main" java.lang.OutOfMemoryError: GC overhead
>> limit exceeded
>> at java.util.LinkedList.linkLast(Unknown Source)
>> at java.util.LinkedList.add(Unknown Source)
>> at java.util.LinkedList.clone(Unknown Source)
>> at
>>
> 
> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:474)
>>
>> at
>>
> 
> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:498)
>>
>> at
>>
> 
> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:470)
>>
>> at
>>
> 
> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:413)
>>
>> at
>>
> 
> org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLearner2.java:551)
>> at
>>
> 
> org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLearner2.java:521)
>> at
>>
> 
> org.dllearner.algorithms.refinement2.ROLearner2.start(ROLearner2.java:436)
>> at
>>
> 
> org.dllearner.algorithms.refinement2.ROLComponent2.start(ROLComponent2.java:441)
>> at org.dllearner.cli.Start.start(Start.java:347)
>> at org.dllearner.cli.Start.main(Start.java:209)
>>
>> Kind regards,
>> Céline
>>
>> Le 10.04.2014 00:02, Lorenz Bühmann a écrit :
>>
>>> Hi Céline,
>>>
>>> of course we can give you more information about DL-Learner if
>>> you're
>>> interested in.
>>>
>>> 1.) I'm not exactly sure what you mean by target language, but if
>>> if you
>>> refer to what's the expressivity of the learned class
>>> expressions, then
>>> no, the target language of DL-Learner is not ALC.
>>> Depending on the used learning algorithm, DL-Learner of course
>>> supports
>>> datatype properties and for example can also learn class
>>> expressions
>>> which consist of constructs used in Description Logics beyond
>>> ALC, like
>>> for example qualified cardinality restrictions(Q).
>>>
>>> 2.) see 1.)
>>>
>>> 3.) We do not have any numbers, but in general the internally
>>> used OWL
>>> reasoner(e.g. Pellet or HermiT) might be a bottleneck. If you're
>>> able to
>>> just load the necessary part of the ontology, this can of course
>>> positively influence the learning process. Maybe we're both
>>> taking about
>>> different things when using the term "noise", but I wouldn't
>>> declare
>>> unnecessary information as noise.
>>>
>>> 4.) Limits in which sense?
>>>
>>> Can you give us any insights into what you're planning to do with
>>> the
>>> DL-Learner?
>>>
>>> Kind regards,
>>> Lorenz
>>> On 04/08/2014 09:00 AM, alec wrote:
>>>
>>>> Hello,
>>>>
>>>> I am a PhD student in Laboratoire de Recherche en Informatique
>>>> in
>>>> Université Paris Sud (France). I have read papers on
>>>> DL-Learner. For my
>>>> thesis project, I might be interested in using an ILP tool to
>>>> learn
>>>> concept definitions. But the ontology I will use as input will
>>>> have
>>>> datatype properties (numerical values) and I would like to use
>>>> a tool
>>>> which can learn
>>>> definitions using these datatype properties.
>>>>
>>>> I would like to have some additional information on DL-Learner
>>>> if it is
>>>> possible. I would be grateful if you could answer my questions.
>>>>
>>>>
>>>> 1. I understood that the target language of your algorithm is
>>>> ALC
>>>> description logic. Can you confirm me that we cannot get a
>>>> definition of
>>>> a concept with datatype properties (other than string datatype
>>>> properties)?
>>>> For example, something like an adult is a person whose age
>>>> hasValue x
>>>> with x>=18.
>>>>
>>>> 2. If I understood right:
>>>> Is there a particular reason for that? Has it a real complexity
>>>> to
>>>> implement? Or do you know tools (open source or free of charge
>>>> for
>>>> academic research) that can generate a definition with
>>>> numerical
>>>> datatype properties (e.g. in SHOIN(D) description logic)?
>>>>
>>>> 3. Are there any constraints about the input ontology? Can it
>>>> be a big
>>>> ontology with potential information which is not interesting
>>>> for
>>>> defining a concept (i.e. with noise)? Or has it to be just the
>>>> interesting part of the ontology?
>>>>
>>>> 4. Can you say what the limits of DL-Learner are?
>>>>
>>>> I would greatly appreciate any help you might be able to give
>>>> me.
>>>>
>>>> Best regards,
>>>> Céline Alec
>>>>
>>>>
>>>
>>
> 
> ------------------------------------------------------------------------------
>>>>
>>>> Put Bad Developers to Shame
>>>> Dominate Development with Jenkins Continuous Integration
>>>> Continuously Automate Build, Test & Deployment
>>>> Start a new project now. Try Jenkins in the cloud.
>>>> http://p.sf.net/sfu/13600_Cloudbees [1]
>>>> _______________________________________________
>>>> dl-learner-discussion mailing list
>>>> dl-...@li... [2]
>>>>
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>>> [3]
>>>
>>>
>>
> 
> ------------------------------------------------------------------------------
>>>
>>> Put Bad Developers to Shame
>>> Dominate Development with Jenkins Continuous Integration
>>> Continuously Automate Build, Test & Deployment
>>> Start a new project now. Try Jenkins in the cloud.
>>> http://p.sf.net/sfu/13600_Cloudbees [4]
>>> _______________________________________________
>>> dl-learner-discussion mailing list
>>> dl-...@li... [5]
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>> [6]
>>
>>
> 
> ------------------------------------------------------------------------------
>> Put Bad Developers to Shame
>> Dominate Development with Jenkins Continuous Integration
>> Continuously Automate Build, Test & Deployment
>> Start a new project now. Try Jenkins in the cloud.
>> http://p.sf.net/sfu/13600_Cloudbees
>>
>> _______________________________________________
>> dl-learner-discussion mailing list
>> dl-...@li...
>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>
>
>
> Links:
> ------
> [1] http://p.sf.net/sfu/13600_Cloudbees
> [2] mailto:dl-...@li...
> [3] 
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
> [4] http://p.sf.net/sfu/13600_Cloudbees
> [5] mailto:dl-...@li...
> [6] 
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
> [7]
> 
> http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-2.tar.gz/download

Re: [DL-Learner discussion] problem with dllearner API parsing configuration file with special characters

[Lorenz B. <spo...@st...>]

Hi June,

thanks for the bug report. Seems like the parser wasn't able to parse 
Unicode characters. I fixed this problem now and will upload a new 
version beta-3 today.
By the way the syntax of your ontology is incorrect. The header must 
contain the namespaces of RDF, OWL etc., i.e. it should look like

<rdf:RDF xml:base="http://dbpedia.org/ontology/" 
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" 
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" 
xmlns:xsd="http://www.w3.org/2001/XMLSchema#" 
xmlns:owl="http://www.w3.org/2002/07/owl#" 
xmlns="http://dbpedia.org/ontology/">

Regards,
Lorenz

On 04/14/2014 04:34 PM, ?? wrote:
> Dear list,
>
> I am using dllearner-1.0-beta-2, and I have some problems on the 
> learning problem defined in the attachment. The configuration file 
> contains some special characters such as French, it seems to me that 
> they cause the following exception. It will be very helpful if you can 
> help me on how to solve the problem. Many thanks!
>
> Exception in thread "main" org.dllearner.confparser3.TokenMgrError: 
> Lexical error at line 7, column 1733.  Encountered: "\u8c0b" (35851), 
> after : "\"http://dbpedia.org/resource/A\ufffd\ufffd 
> <http://dbpedia.org/resource/A%5Cufffd%5Cufffd>"
> at 
> org.dllearner.confparser3.ConfParserTokenManager.getNextToken(ConfParserTokenManager.java:507)
> at org.dllearner.confparser3.ConfParser.jj_scan_token(ConfParser.java:617)
> at org.dllearner.confparser3.ConfParser.jj_3R_6(ConfParser.java:445)
> at org.dllearner.confparser3.ConfParser.jj_3_1(ConfParser.java:481)
> at org.dllearner.confparser3.ConfParser.jj_2_1(ConfParser.java:345)
> at org.dllearner.confparser3.ConfParser.ConfOption(ConfParser.java:153)
> at org.dllearner.confparser3.ConfParser.Start(ConfParser.java:79)
> at 
> org.dllearner.confparser3.ConfParserConfiguration.<init>(ConfParserConfiguration.java:40)
> at org.dllearner.cli.CLI.main(CLI.java:174)
>
>
>
> Best wishes,
>
> June
>
>
> ------------------------------------------------------------------------------
> Learn Graph Databases - Download FREE O'Reilly Book
> "Graph Databases" is the definitive new guide to graph databases and their
> applications. Written by three acclaimed leaders in the field,
> this first edition is now available. Download your free book today!
> http://p.sf.net/sfu/NeoTech
>
>
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

Re: [DL-Learner discussion] Questions on DL-Learner

[Lorenz B. <spo...@st...>]

Hi,

which version if DL-Learner do you use? The latest version online is 
http://sourceforge.net/projects/dl-learner/files/DL-Learner/dllearner-1.0-beta-2.tar.gz/download 
but we plan to upload a new one as there are many new features and 
bugfixes in the current SVN version.
Form the error message, I assume you use a quite old version. Can you 
try the latest version?

According to the modified example:

You have as positive examples:
stefan -> male, 28
markus -> male, 50
martin -> male, 34
and negative examples:
heinz -> male, 17
anna -> female, 10
michelle -> female, 4

So I guess you want to learn something like
"male AND hasAge integer[>=34]" ?

I'll check if this works, but it should be possible.

Regards,
Lorenz
On 04/10/2014 10:53 AM, alec wrote:
> Hi Lorenz,
>
> Thank you very much for your answers.
> I'm planning to use DL-Learner to learn concept definitions from an 
> ontology of holiday destinations (I don't have the ontology yet). I 
> want to make sure it is possible to get definitions with 
> inferiority/superiority signs (about numerical datatype properties not 
> about cardinality restrictions).
> For example, I would like to get something like that:
> "Definition of a destination which is hot in Winter:
> hasJanuaryTemperature x and
> hasFebruaryTemperature y and
> hasMarchTemperature z and
> x>20 and
> y>20 and
> z>20".
>
> I tried to modify the "father.owl" file (see attachments) in 
> DL-Learner examples. I put a "hasAge" datatype property and I deleted 
> "hasChild". I was hoping to see if I could get a definition with a 
> superiority/inferiority sign about age. I got that:
>
> DL-Learner 2010-08-07 command line interface
> starting component manager ... OK (82ms)
> initialising component "OWL file" ... OK (0ms)
> initialising component "fast instance checker" ... OK (388ms)
> initialising component "pos neg learning problem" ... OK (0ms)
> initialising component "OCEL" ... OK (14ms)
>
> starting top down refinement with: Thing (50% accuracy)
> more accurate (83,33%) class expression found: male
> Exception in thread "main" java.lang.OutOfMemoryError: GC overhead 
> limit exceeded
>         at java.util.LinkedList.linkLast(Unknown Source)
>         at java.util.LinkedList.add(Unknown Source)
>         at java.util.LinkedList.clone(Unknown Source)
>         at 
> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:474)
>         at 
> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:498)
>         at 
> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:470)
>         at 
> org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:413)
>         at 
> org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLearner2.java:551)
>         at 
> org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLearner2.java:521)
>         at 
> org.dllearner.algorithms.refinement2.ROLearner2.start(ROLearner2.java:436)
>         at 
> org.dllearner.algorithms.refinement2.ROLComponent2.start(ROLComponent2.java:441)
>         at org.dllearner.cli.Start.start(Start.java:347)
>         at org.dllearner.cli.Start.main(Start.java:209)
>
>
> Kind regards,
> Céline
>
>
> Le 10.04.2014 00:02, Lorenz Bühmann a écrit :
>> Hi Céline,
>>
>> of course we can give you more information about DL-Learner if you're
>> interested in.
>>
>> 1.) I'm not exactly sure what you mean by target language, but if if you
>> refer to what's the expressivity of the learned class expressions, then
>> no, the target language of DL-Learner is not ALC.
>> Depending on the used learning algorithm, DL-Learner of course supports
>> datatype properties and for example can also learn class expressions
>> which consist of constructs used in Description Logics beyond ALC, like
>> for example qualified cardinality restrictions(Q).
>>
>> 2.) see 1.)
>>
>> 3.) We do not have any numbers, but in general the internally used OWL
>> reasoner(e.g. Pellet or HermiT) might be a bottleneck. If you're able to
>> just load the necessary part of the ontology, this can of course
>> positively influence the learning process. Maybe we're both taking about
>> different things when using the term "noise", but I wouldn't declare
>> unnecessary information as noise.
>>
>> 4.) Limits in which sense?
>>
>> Can you give us any insights into what you're planning to do with the
>> DL-Learner?
>>
>>
>> Kind regards,
>> Lorenz
>> On 04/08/2014 09:00 AM, alec wrote:
>>> Hello,
>>>
>>> I am a PhD student in Laboratoire de Recherche en Informatique in
>>> Université Paris Sud (France). I have read papers on DL-Learner. For my
>>> thesis project, I might be interested in using an ILP tool to learn
>>> concept definitions. But the ontology I will use as input will have
>>> datatype properties (numerical values) and I would like to use a tool
>>> which can learn
>>> definitions using these datatype properties.
>>>
>>> I would like to have some additional information on DL-Learner if it is
>>> possible. I would be grateful if you could answer my questions.
>>>
>>> 1. I understood that the target language of your algorithm is ALC
>>> description logic. Can you confirm me that we cannot get a 
>>> definition of
>>> a concept with datatype properties (other than string datatype
>>> properties)?
>>> For example, something like an adult is a person whose age hasValue x
>>> with x>=18.
>>>
>>> 2. If I understood right:
>>> Is there a particular reason for that? Has it a real complexity to
>>> implement? Or do you know tools (open source or free of charge for
>>> academic research) that can generate a definition with numerical
>>> datatype properties (e.g. in SHOIN(D) description logic)?
>>>
>>> 3. Are there any constraints about the input ontology? Can it be a big
>>> ontology with potential information which is not interesting for
>>> defining a concept (i.e. with noise)? Or has it to be just the
>>> interesting part of the ontology?
>>>
>>> 4. Can you say what the limits of DL-Learner are?
>>>
>>> I would greatly appreciate any help you might be able to give me.
>>>
>>> Best regards,
>>> Céline Alec
>>>
>>>
>>> ------------------------------------------------------------------------------ 
>>>
>>> Put Bad Developers to Shame
>>> Dominate Development with Jenkins Continuous Integration
>>> Continuously Automate Build, Test & Deployment
>>> Start a new project now. Try Jenkins in the cloud.
>>> http://p.sf.net/sfu/13600_Cloudbees
>>> _______________________________________________
>>> dl-learner-discussion mailing list
>>> dl-...@li...
>>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>>
>>
>>
>> ------------------------------------------------------------------------------ 
>>
>> Put Bad Developers to Shame
>> Dominate Development with Jenkins Continuous Integration
>> Continuously Automate Build, Test & Deployment
>> Start a new project now. Try Jenkins in the cloud.
>> http://p.sf.net/sfu/13600_Cloudbees
>> _______________________________________________
>> dl-learner-discussion mailing list
>> dl-...@li...
>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>
>
> ------------------------------------------------------------------------------
> Put Bad Developers to Shame
> Dominate Development with Jenkins Continuous Integration
> Continuously Automate Build, Test & Deployment
> Start a new project now. Try Jenkins in the cloud.
> http://p.sf.net/sfu/13600_Cloudbees
>
>
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

Re: [DL-Learner discussion] Questions on DL-Learner

[alec <Cel...@lr...>]

Hi Lorenz,

Thank you very much for your answers.
I'm planning to use DL-Learner to learn concept definitions from an 
ontology of holiday destinations (I don't have the ontology yet). I want 
to make sure it is possible to get definitions with 
inferiority/superiority signs (about numerical datatype properties not 
about cardinality restrictions).
For example, I would like to get something like that:
"Definition of a destination which is hot in Winter:
hasJanuaryTemperature x and
hasFebruaryTemperature y and
hasMarchTemperature z and
x>20 and
y>20 and
z>20".

I tried to modify the "father.owl" file (see attachments) in DL-Learner 
examples. I put a "hasAge" datatype property and I deleted "hasChild". I 
was hoping to see if I could get a definition with a 
superiority/inferiority sign about age. I got that:

DL-Learner 2010-08-07 command line interface
starting component manager ... OK (82ms)
initialising component "OWL file" ... OK (0ms)
initialising component "fast instance checker" ... OK (388ms)
initialising component "pos neg learning problem" ... OK (0ms)
initialising component "OCEL" ... OK (14ms)

starting top down refinement with: Thing (50% accuracy)
more accurate (83,33%) class expression found: male
Exception in thread "main" java.lang.OutOfMemoryError: GC overhead 
limit exceeded
         at java.util.LinkedList.linkLast(Unknown Source)
         at java.util.LinkedList.add(Unknown Source)
         at java.util.LinkedList.clone(Unknown Source)
         at 
org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:474)
         at 
org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:498)
         at 
org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:470)
         at 
org.dllearner.refinementoperators.RhoDRDown.refine(RhoDRDown.java:413)
         at 
org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLearner2.java:551)
         at 
org.dllearner.algorithms.refinement2.ROLearner2.extendNodeProper(ROLearner2.java:521)
         at 
org.dllearner.algorithms.refinement2.ROLearner2.start(ROLearner2.java:436)
         at 
org.dllearner.algorithms.refinement2.ROLComponent2.start(ROLComponent2.java:441)
         at org.dllearner.cli.Start.start(Start.java:347)
         at org.dllearner.cli.Start.main(Start.java:209)


Kind regards,
Céline


Le 10.04.2014 00:02, Lorenz Bühmann a écrit :
> Hi Céline,
>
> of course we can give you more information about DL-Learner if you're
> interested in.
>
> 1.) I'm not exactly sure what you mean by target language, but if if 
> you
> refer to what's the expressivity of the learned class expressions, 
> then
> no, the target language of DL-Learner is not ALC.
> Depending on the used learning algorithm, DL-Learner of course 
> supports
> datatype properties and for example can also learn class expressions
> which consist of constructs used in Description Logics beyond ALC, 
> like
> for example qualified cardinality restrictions(Q).
>
> 2.) see 1.)
>
> 3.) We do not have any numbers, but in general the internally used 
> OWL
> reasoner(e.g. Pellet or HermiT) might be a bottleneck. If you're able 
> to
> just load the necessary part of the ontology, this can of course
> positively influence the learning process. Maybe we're both taking 
> about
> different things when using the term "noise", but I wouldn't declare
> unnecessary information as noise.
>
> 4.) Limits in which sense?
>
> Can you give us any insights into what you're planning to do with the
> DL-Learner?
>
>
> Kind regards,
> Lorenz
> On 04/08/2014 09:00 AM, alec wrote:
>> Hello,
>>
>> I am a PhD student in Laboratoire de Recherche en Informatique in
>> Université Paris Sud (France). I have read papers on DL-Learner. For 
>> my
>> thesis project, I might be interested in using an ILP tool to learn
>> concept definitions. But the ontology I will use as input will have
>> datatype properties (numerical values) and I would like to use a 
>> tool
>> which can learn
>> definitions using these datatype properties.
>>
>> I would like to have some additional information on DL-Learner if it 
>> is
>> possible. I would be grateful if you could answer my questions.
>>
>> 1. I understood that the target language of your algorithm is ALC
>> description logic. Can you confirm me that we cannot get a 
>> definition of
>> a concept with datatype properties (other than string datatype
>> properties)?
>> For example, something like an adult is a person whose age hasValue 
>> x
>> with x>=18.
>>
>> 2. If I understood right:
>> Is there a particular reason for that? Has it a real complexity to
>> implement? Or do you know tools (open source or free of charge for
>> academic research) that can generate a definition with numerical
>> datatype properties (e.g. in SHOIN(D) description logic)?
>>
>> 3. Are there any constraints about the input ontology? Can it be a 
>> big
>> ontology with potential information which is not interesting for
>> defining a concept (i.e. with noise)? Or has it to be just the
>> interesting part of the ontology?
>>
>> 4. Can you say what the limits of DL-Learner are?
>>
>> I would greatly appreciate any help you might be able to give me.
>>
>> Best regards,
>> Céline Alec
>>
>> 
>> ------------------------------------------------------------------------------
>> Put Bad Developers to Shame
>> Dominate Development with Jenkins Continuous Integration
>> Continuously Automate Build, Test & Deployment
>> Start a new project now. Try Jenkins in the cloud.
>> http://p.sf.net/sfu/13600_Cloudbees
>> _______________________________________________
>> dl-learner-discussion mailing list
>> dl-...@li...
>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion
>
>
> 
> ------------------------------------------------------------------------------
> Put Bad Developers to Shame
> Dominate Development with Jenkins Continuous Integration
> Continuously Automate Build, Test & Deployment
> Start a new project now. Try Jenkins in the cloud.
> http://p.sf.net/sfu/13600_Cloudbees
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

Re: [DL-Learner discussion] Questions on DL-Learner

[Lorenz B. <spo...@st...>]

Hi Céline,

of course we can give you more information about DL-Learner if you're 
interested in.

1.) I'm not exactly sure what you mean by target language, but if if you 
refer to what's the expressivity of the learned class expressions, then 
no, the target language of DL-Learner is not ALC.
Depending on the used learning algorithm, DL-Learner of course supports 
datatype properties and for example can also learn class expressions 
which consist of constructs used in Description Logics beyond ALC, like 
for example qualified cardinality restrictions(Q).

2.) see 1.)

3.) We do not have any numbers, but in general the internally used OWL 
reasoner(e.g. Pellet or HermiT) might be a bottleneck. If you're able to 
just load the necessary part of the ontology, this can of course 
positively influence the learning process. Maybe we're both taking about 
different things when using the term "noise", but I wouldn't declare 
unnecessary information as noise.

4.) Limits in which sense?

Can you give us any insights into what you're planning to do with the 
DL-Learner?


Kind regards,
Lorenz
On 04/08/2014 09:00 AM, alec wrote:
> Hello,
>
> I am a PhD student in Laboratoire de Recherche en Informatique in
> Université Paris Sud (France). I have read papers on DL-Learner. For my
> thesis project, I might be interested in using an ILP tool to learn
> concept definitions. But the ontology I will use as input will have
> datatype properties (numerical values) and I would like to use a tool
> which can learn
> definitions using these datatype properties.
>
> I would like to have some additional information on DL-Learner if it is
> possible. I would be grateful if you could answer my questions.
>
> 1. I understood that the target language of your algorithm is ALC
> description logic. Can you confirm me that we cannot get a definition of
> a concept with datatype properties (other than string datatype
> properties)?
> For example, something like an adult is a person whose age hasValue x
> with x>=18.
>
> 2. If I understood right:
> Is there a particular reason for that? Has it a real complexity to
> implement? Or do you know tools (open source or free of charge for
> academic research) that can generate a definition with numerical
> datatype properties (e.g. in SHOIN(D) description logic)?
>
> 3. Are there any constraints about the input ontology? Can it be a big
> ontology with potential information which is not interesting for
> defining a concept (i.e. with noise)? Or has it to be just the
> interesting part of the ontology?
>
> 4. Can you say what the limits of DL-Learner are?
>
> I would greatly appreciate any help you might be able to give me.
>
> Best regards,
> Céline Alec
>
> ------------------------------------------------------------------------------
> Put Bad Developers to Shame
> Dominate Development with Jenkins Continuous Integration
> Continuously Automate Build, Test & Deployment
> Start a new project now. Try Jenkins in the cloud.
> http://p.sf.net/sfu/13600_Cloudbees
> _______________________________________________
> dl-learner-discussion mailing list
> dl-...@li...
> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion

[DL-Learner discussion] Questions on DL-Learner

[alec <Cel...@lr...>]

Hello,

I am a PhD student in Laboratoire de Recherche en Informatique in 
Université Paris Sud (France). I have read papers on DL-Learner. For my 
thesis project, I might be interested in using an ILP tool to learn 
concept definitions. But the ontology I will use as input will have 
datatype properties (numerical values) and I would like to use a tool 
which can learn
definitions using these datatype properties.

I would like to have some additional information on DL-Learner if it is 
possible. I would be grateful if you could answer my questions.

1. I understood that the target language of your algorithm is ALC 
description logic. Can you confirm me that we cannot get a definition of 
a concept with datatype properties (other than string datatype 
properties)?
For example, something like an adult is a person whose age hasValue x 
with x>=18.

2. If I understood right:
Is there a particular reason for that? Has it a real complexity to 
implement? Or do you know tools (open source or free of charge for 
academic research) that can generate a definition with numerical 
datatype properties (e.g. in SHOIN(D) description logic)?

3. Are there any constraints about the input ontology? Can it be a big 
ontology with potential information which is not interesting for 
defining a concept (i.e. with noise)? Or has it to be just the 
interesting part of the ontology?

4. Can you say what the limits of DL-Learner are?

I would greatly appreciate any help you might be able to give me.

Best regards,
Céline Alec

[DL-Learner discussion] Problems to Build

[Bruno C. <co...@li...>]

Hi,


I am trying to include a dependency of DL-Learner to my maven project in NetBeans.

I follow the instructions shown in the DL-Learner web site to do so.

However, when I try to build my Project, I get the following error:


cd C:\Users\Bruno\Projetos\dl-learner-ensemble; "JAVA_HOME=C:\\Program Files\\Java\\jdk1.7.0_40" cmd /c "\"\"C:\\Program Files\\NetBeans 7.4\\java\\maven\\bin\\mvn.bat\" -Dexec.args=\"-classpath %classpath edu.uff.dllearnerensemble.App\" -Dexec.executable=\"C:\\Program Files\\Java\\jdk1.7.0_40\\bin\\java.exe\" -DnetbeansProjectMappings= -Dmaven.ext.class.path=\"C:\\Program Files\\NetBeans 7.4\\java\\maven-nblib\\netbeans-eventspy.jar\" org.codehaus.mojo:exec-maven-plugin:1.2.1:exec\""
Running NetBeans Compile On Save execution. Phase execution is skipped and output directories of dependency projects (with Compile on Save turned on) will be used instead of their jar artifacts.
Scanning for projects...
                                                                        
------------------------------------------------------------------------
Building dl-learner-ensemble 1.0-SNAPSHOT
------------------------------------------------------------------------
Downloading: http://prod1.aksw.org:8081/archiva/repository/snapshots/org/dllearner/components-core/1.0-SNAPSHOT/maven-metadata.xml


Could not transfer metadata org.dllearner:components-core:1.0-SNAPSHOT/maven-metadata.xml from/to snapshot.maven.aksw (http://prod1.aksw.org:8081/archiva/repository/snapshots/): Connection to http://prod1.aksw.org:8081 refused
Failure to transfer org.dllearner:components-core:1.0-SNAPSHOT/maven-metadata.xml from http://prod1.aksw.org:8081/archiva/repository/snapshots/ was cached in the local repository, resolution will not be reattempted until the update interval of snapshot.maven.aksw has elapsed or updates are forced. Original error: Could not transfer metadata org.dllearner:components-core:1.0-SNAPSHOT/maven-metadata.xml from/to snapshot.maven.aksw (http://prod1.aksw.org:8081/archiva/repository/snapshots/): Connection to http://prod1.aksw.org:8081 refused
Downloading: http://prod1.aksw.org:8081/archiva/repository/snapshots/org/dllearner/components-core/1.0-SNAPSHOT/components-core-1.0-SNAPSHOT.pom


------------------------------------------------------------------------
BUILD FAILURE
------------------------------------------------------------------------
Total time: 42.609s
Finished at: Tue Mar 11 23:58:11 BRT 2014
Final Memory: 5M/123M
------------------------------------------------------------------------
Failed to execute goal on project dl-learner-ensemble: Could not resolve dependencies for project edu.uff:dl-learner-ensemble:jar:1.0-SNAPSHOT: Failed to collect dependencies for [junit:junit:jar:3.8.1 (test), org.dllearner:components-core:jar:1.0-SNAPSHOT (compile)]: Failed to read artifact descriptor for org.dllearner:components-core:jar:1.0-SNAPSHOT: Could not transfer artifact org.dllearner:components-core:pom:1.0-SNAPSHOT from/to snapshot.maven.aksw (http://prod1.aksw.org:8081/archiva/repository/snapshots/): Connection to http://prod1.aksw.org:8081 refused: Connection timed out: connect -> [Help 1]


To see the full stack trace of the errors, re-run Maven with the -e switch.
Re-run Maven using the -X switch to enable full debug logging.


For more information about the errors and possible solutions, please read the following articles:
[Help 1] http://cwiki.apache.org/confluence/display/MAVEN/DependencyResolutionException



Apparently I am having my connection refused.

Am I doing something wrong?


Thank you,

Bruno Coimbra






Enviado do Email do Windows